## Abstract

Support Vector Data Description (SVDD) is a popular one-class classifier for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy. In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.

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## 1 Introduction

Support Vector Data Description (SVDD) is one of the most popular and actively researched one-class classifiers for anomaly and novelty detection (Tax and Duin 2004; Liu et al. 2010; Trittenbach et al. 2018). The basic variant of SVDD is an unsupervised classifier that fits a tight hypersphere around the majority of observations, the inliers, to distinguish them from irregular observations, the outliers. Despite its resounding success, a downside is that SVDD and its progeny do not scale well with data size (Trittenbach et al. 2019b). Even efficient solvers like decomposition methods (Chaudhuri et al. 2018; Chu et al. 2004; Kim et al. 2007; Platt 1998) result in training times prohibitive for many applications. In these cases, sampling for data reduction is essential (Li 2011; Hu et al. 2014; Li et al. 2019; Alam et al. 2020; Sun et al. 2016; Qu et al. 2019; Li et al. 2018; Xiao et al. 2014; Zhu et al. 2014; Krawczyk et al. 2019).

One of the defining characteristics of SVDD is that only a few observations, the support vectors, define a decision boundary. Thus, a good sample is one for which SVDD selects support vectors similar to the original ones, i.e., the ones obtained on the full data set. This has spurred the design of sampling methods that try to identify support-vector candidates in the original data, to retain them in the sample (Li 2011; Li et al. 2019; Qu et al. 2019; Hu et al. 2014; Alam et al. 2020; Li et al. 2018; Xiao et al. 2014; Zhu et al. 2014). A common approach is to select so-called “boundary points” as support-vector candidates, e.g., observations that are dissimilar to each other (Li 2011; Zhu et al. 2014).

But calibrating existing methods such that they indeed identify boundary points is difficult. A reason is that the sample they return depends significantly on the choice of exogenous parameters, and selecting suitable parameter values is not intuitive (see Sect. 5). A further shortcoming is that including all boundary points in a sample does not guarantee SVDD training to indeed yield the original support vectors. The issue is that selection of support vectors hinges on other aspects, such as the ratio between inliers and outliers in the sample and a sufficient number of non-boundary observations in the sample. Disregarding them may, for instance, fragment contiguous inlier regions and yield wrong outlier classifications after sampling, see Fig. 1. The influence of these aspects on SVDD is known, but their effects on sample selection are not well studied. It is an open question how to select a sample where SVDD indeed approximates the original decision boundary. Finally, a point largely orthogonal to these issues is that there also is very limited experimental comparison among competitors. This makes an empirical selection of suitable SVDD sampling methods difficult as well.

*Contributions* In this article, we propose a novel way to SVDD sampling. We make three contributions. First, we reduce SVDD sampling to a decision-theoretic problem of separating data using empirical density values. Based on this reduction, we formulate SVDD sampling as a constrained optimization problem. Its objective is to find a minimal sample where the density of all observations of the data set is close-to-uniform. We provide theoretical justification that a sample obtained in this way i) prevent a fragmentation of the inlier regions, and ii) retain the observations necessary to identify the original support vectors.

Second, we propose __R__educing s__A__mples by __P__runing of __I__nlier __D__ensities (RAPID), an efficient algorithm to solve the optimization. RAPID is the first SVDD sampling algorithm with theoretical guarantees on retaining the original decision boundaries. RAPID does not require any parameters in addition to the ones already required by SVDD. This lets RAPID stand out from existing methods, which all hinge on mostly unintuitive, exogenous parameters. RAPID further is easy to implement, and scales well to very large data sets.

Third, we conduct the – by far – most comprehensive comparison of SVDD sampling methods. We compare RAPID against 8 methods on 23 real-world and 85 synthetic data sets. In all experiments, RAPID consistently produces a small sample with high classification quality. Overall, RAPID outperforms all of its competitors in the trade-off between algorithm runtime, sample size, and classification accuracy, often by an order of magnitude.

## 2 Fundamentals

The data mining community differentiates between *lazy* and *eager* learners (Aggarwal 2015a). This differentiation is available for outlier detection as well. There, *lazy* learners perform instance-based learning by defining measures of “outlierness” of an observation (Aggarwal 2015b). *Lazy* learners delay the learning until predicting the class of an observation. For an overview and experimental comparison of *lazy* learners we refer to Campos et al. (2016). For *eager* learners, the computational effort takes place before the predictions, since they do construct a classification model. *Eager* learners perform explicit generalization, and the classification of new observations tends to be much faster than for *lazy* learners (Aggarwal 2015a). In our article, we focus on the most popular eager learners for outlier detection, Support Vector Data Description (SVDD) (Tax and Duin 2004).

The objective of SVDD is to learn a description of a set of observations, the *target*. A good description allows to distinguish the target from other, non-target observations. In our article, we focus on unsupervised outlier detection. So the targets, i.e., the class that SVDD explicitly learns, are inliers, and the non-targets are outliers. However, one does not have any labels available when learning an SVDD classifier, i.e., the learning scenario is unsupervised. First, we introduce preliminaries and then the SVDD optimization problem.

*Preliminaries* Let \(\mathbf {X} = \langle x_1, x_2, \dots , x_N \rangle\) be a data set of *N* *observations* from the domain \(\mathbb {X} = \mathbb {R}^M\) where *M* is the number of dimensions. A *sample* is a subset \(\mathbf {S} \subseteq \mathbf {X}\) of the data set with sampling ratio \({\vert \mathbf {S} \vert }/{N}\). Further, we denote \(x \in \mathbf {S}\) as *selected*, and \(x \notin \mathbf {S}\) as *not-selected* observations. The probability density of \(\mathbf {X}\) is *p*(*x*). Further, let \(\mathbf {Y} = \langle y_1, y_2, \dots , y_N \rangle\) be a ground truth, i.e., each entry is the realization of a dichotomous variable \(\mathbb {Y} = \{\text {in}, \text {out}\}\). The ground truth densities are the conditional probability densities \(p_{\text {inlier}}(x) = P(\mathbf {X} = x \mid \mathbf {Y} = \text {in})\), and \(p_{\text {outlier}}(x) = P(\mathbf {X} = x \mid \mathbf {Y} = \text {out})\) respectively. One can estimate the empirical density of \(\mathbf {X}\) by kernel density estimation.

where *k* is a kernel function with \(k(x,x) = 1\). A popular choice is the Gaussian kernel \(k_\gamma (x, x') = e^{- \gamma \Vert x - x' \Vert }\), where \(\gamma \ge 0\) is the parameter to control the kernel bandwidth. We use the shorthand \(d_x = d_{\mathbf {X}}(x)\) when the reference to \(\mathbf {X}\) is unambiguous. Note that \(d_{\mathbf {X}}\) requires normalization further to represent a probability density. Densities can be used to characterize observations in different ways.

###
**Definition 1**

(Level Set) A level set is a set of observations with equal density \(L_\theta :=\{x \in \mathbf {X} :d_x = \theta \}\). A super-level set is a set of observations with \(L^{+}_{\theta } :=\{x \in \mathbf {X} :d_x \ge \theta \}\).

One way to use level sets to categorize observations is to define a *level-set classifier* as a function of type \(g:\mathbb {X} \rightarrow \mathbb {Y}\) with

Another useful categorization is to separate observations into boundary points and inner points. There are different ways to define a boundary of \(\mathbf {X}\) (Li 2011; Hu et al. 2014; Li et al. 2019; Qu et al. 2019; Alam et al. 2020; Li et al. 2018; Xiao et al. 2014; Zhu et al. 2014). For this article, we define boundary points as observations with density values close to the minimum empirical density.

###
**Definition 2**

(Boundary Point) Let \(d_\text {min} = \min _{x \in \mathbf {X}} d_x\), and let \(\delta\) be a small positive value. An observation \(x \in \mathbf {X}\) is a boundary point of \(\mathbf {X}\) if \(x \in \mathbf {B}^\mathbf {X}\) with \(\mathbf {B}^\mathbf {X} = L^{+}_{d_\text {min}} \setminus L^{+}_{(d_\text {min} + \delta )}\).

*SVDD Classifier*SVDD (Tax and Duin 2004) is a quadratic optimization problem that searches for a minimum enclosing hypersphere with center *a* and radius *R* around the data. The linear formulation of the optimization problem is

with cost parameter *C* and slack variables \(\varvec{\xi }\). Solving SVDD gives a fixed *a* and *R* and a decision function

When solving SVDD in the dual space, \(f^\mathbf {X}\) only relies on inner product calculations between *x* and some of the training observations, the support vectors. So classification with SVDD is efficient if the number of support vectors is low. Also note that under mild assumptions, SVDD is equivalent to \(\nu\)-SVM (Schölkopf et al. 2001).

SVDD has two hyperparameters, *C* and a kernel function *k*. \(C \in \mathbb {R}_{[0,1]}\) is a trade-off parameter. It allows some observations in the training data to fall outside the hypersphere if this reduces the radius significantly. Formally, observations outside the hypersphere with positive slack \(\xi > 0\) are weighted by a cost *C*. High values for *C* make excluding observations expensive; based on the dual of SVDD, one can see that if \(C=1\), SVDD degenerates to a hard-margin classifier (Tax and Duin 2004).

To allow decision boundaries of arbitrary shape, one can use the well-known kernel trick to replace inner products in the dual of SVDD by a kernel function *k*. The most popular kernel with SVDD is the Gaussian kernel. Its bandwidth parameter \(\gamma\) controls the flexibility of the decision boundary. For \(\gamma \! \rightarrow \! 0\), the decision boundary in the data space approximates a hypersphere. Choosing good values for the two hyperparameters \(\gamma\) and *C* is difficult (Liao et al. 2018). There is no established way of setting the parameter values, and one must choose one of the many heuristics to tune SVDD in an unsupervised setting (Scott 2015; Liao et al. 2018; Tax and Duin 2004; Trittenbach et al. 2019a).

## 3 Related work

SVDD is a quadratic problem (QP). The time complexity of solving SVDD is in \(\mathcal {O}(N^3)\) (Chu et al. 2004). Thus, training does not scale well to large data sets. However, the time complexity for classification is only linear in the number of support vectors. So for large *N*, training time is much larger than classification time. Still, long classification times may be an issue, e.g., in time-critical applications. So curbing the runtimes has long become an important topic in the SVDD literature. In Sect. 3.1, we categorize existing approaches that focus on SVDD speedup, see Fig. 2 for an overview. In Sect. 3.2, we then turn to *Sampling*, the category our current article belongs to.

### 3.1 Categorization

We distinguish between *Fast Training* and *Fast Classification*.

*Fast Training* To speed up training of SVDD, one has two options: reduction of the problem size, and optimization of the solver. For *Reduction*, one can distinguish further: A first type reduces the number of observations by *Sampling*. This is the category of methods mentioned in our introduction (Alam et al. (2020); Hu et al. (2014); Krawczyk et al. (2019); Li (2011); Qu et al. (2019); Li et al. (2018, 2019); Sun et al. (2016); Xiao et al. (2014); Zhu et al. (2014)). A second type reduces the size of the *Kernel matrix*, e.g., by approximation (Schölkopf et al. 2000; Achlioptas et al. 2002; Fine and Scheinberg 2001; Nguyen et al. 2008). Examples are the Nyström-method (Williams and Seeger 2001) and choosing random Fourier features (Yang et al. 2012).

*Optimization* on the other hand decomposes QP into smaller chunks that can be solved efficiently. Literature features methods that decompose with clustering (Kim et al. 2007) and with multiple random subsets (Chaudhuri et al. 2018). The most widely used decomposition methods are sequential minimal optimization (SMO) (Platt 1998) and its variants. These methods iteratively divide SVDD into small QP sub-problems and solve them analytically. Finally, there are core-set method that expands the decision boundary by iteratively updating an SVDD solution (Chu et al. 2004; Chen and Li 2019). Core-set approaches are (\(1+\varepsilon\)) approximations, i.e., they may not find the exact decision boundary, given training data.

Reduction and Optimization are orthogonal to each other. Thus, one can use problem-size reduction in a *pre-processing* step before solving SVDD efficiently.

*Fast Classification* When SVDD uses a non-linear kernel, one cannot compute the pre-image of the center *a*. Instead, one must compute the distance of an observation to *a* by a linear combination of the support vectors in the kernel space. However, literature proposes several approaches to approximate the pre-image of *a* (Mika et al. 1999; Kwok and Tsang 2004; Bakır et al. 2004; Liu et al. 2010; Peng and Xu 2012). With this, classification no longer depends on the support vectors, and is in \(\mathcal {O}(1)\). Fast Classification is orthogonal to Fast Training, i.e., it can come as a *post-processing* step, after training.

### 3.2 Sampling methods

Sampling methods take the original data \(\mathbf {X}\) set as an input and produce a sample \(\mathbf {S}\). All existing sampling methods assume the *target-only scenario*, i.e., all observations in \(\mathbf {X}\) are from the target class. This is equivalent to a supervised setting where one has knowledge of the ground truth, and \(\mathbf {Y} = \langle \text {in}, \text {in}, \dots , \text {in} \rangle\). Thus, most of the competitors therefore require modifications to apply to the outlier scenario, see Sect. 4.1 for details. In the following, we discuss existing sampling methods for the *target-only scenario*. We categorize them into different types: *Edge-point* detectors, *Pruning* methods and *Others*. Table 1 provides an overview.

*Edge-point* Most sampling approaches focus on selecting observations that demarcate \(p_{\text {inlier}}\) from \(p_{\text {outlier}}\), and therefore are expected to be support vectors. Such observations are called “edge points” or “boundary points”. Literature proposes different ways to identify edge points. One idea is to use the angle between an observation and its *k* nearest neighbors (Li 2011; Zhu et al. 2014) as an indication. An observation is selected as edge point if most of its neighbors lie within a small, convex cone with the observation as the apex. One has to specify a threshold for the share of neighbors and the width of the cone (Li 2011) as exogenous parameters. Others suggest to identify edge points through a farthest neighbor search. For instance, one suggestion is to first sort the observations by decreasing distance to its k-farthest neighbors (KFN) (Xiao et al. 2014), and then select the top \(\varepsilon\) percent as edge points. The rationale presented in the paper is that inner points are expected to have a lower KFN distance than edge points. A more recent variant uses angle-based search (Alam et al. 2020). The idea of the paper is to initialize the method by the mean over all observations as the apex and divide the space into a pre-specified number of cones. For each cone, one only keeps the farthest observation as edge points.

Next, there are methods that select edge points by density-based outlier rankings, e.g., DBSCAN (Li et al. 2019) and LOF (Hu et al. 2014). Here, the assumption is that edge points occur in sparse regions of the data space. A similar idea is to rank observations with a high distance to all other observations (Li et al. 2018). Others have suggested to rank observation highly if they have low density and a large distance to high-density observations (Qu et al. 2019). Naturally, ranking methods require to set a cutoff value to distinguish edge points from other observations.

*Pruning* The idea of pruning is to iteratively remove observations from high-density regions as long as the sample remains “density-connected”. One way to achieve this is by pruning all neighbors of an observation closer than a minimum distance, starting from the observation closest to the cluster mean (Sun et al. 2016). Yet this approach requires to set the minimum distance threshold, and a good choice is data dependent.

*Others* There is one method that differs significantly from the other ones (Krawczyk et al. 2019). The basic idea is to generate artificial outliers to transform the problem into a binary classification problem. Based on the augmented data, one can apply conventional sampling methods such as binary instance reduction. The sampling method then relies on an evolutionary algorithm where the fitness function is the prediction quality on the augmented data. Finally, the method only retains the remaining inliers and discards all artificial observations. However, this requires to solve many SVDD instances in each iteration.

To summarize, there are many methods to select a sample for SVDD. However, they are based upon some intuition regarding the SVDD and do not come with any formal guarantee. Edge point detectors in particular return a poor sample in some cases, since they do not guarantee coherence of a selected sample, see Fig. 1. Further, all existing approaches require to set some exogenous parameter. But the influence of the parameter values on the sample is difficult to grasp. Finally, existing sampling methods are designed for the *target-only scenario*. It is unclear whether they can be modified to work well with the *outlier scenario*.

## 4 Density-based sampling for SVDD

In this section, we present an efficient and effective sampling method for scaling SVDD to very large data sets. In a nutshell, we exploit that an SVDD decision boundary is in fact a level-set estimate (Vert and Vert 2006), and that inliers are a super-level set. The idea behind our sampling method is to remove observations from a data set such that the inlier super-level set does not change. To this end, we show that for the Gaussian kernel *the super-level set of inliers does not change as long as not-selected observations have higher density than the minimum density of selected observations*. If this *density rule* is violated, sampling may produce “gaps”, i.e., regions of inliers that become regions of outliers. Such gaps curb the SVDD quality. Thus, we strive for a sample of minimal size that satisfies the density rule.

Figure 3 illustrates our approach. In a first step, we separate the unlabeled data into outlier and inlier regions based on their empirical density, see Sect. 4.1. We then frame sample selection as a optimization problem where the constraints enforce the density rule in Sect. 4.2. In Sects. 4.3 we propose RAPID, an efficient and easy-to-implement algorithm to solve the optimization problem. RAPID returns a small sample which has a close-to-uniform density, i.e., a small sample that still obeys the density rule, and also contains the boundary points of the original data.

### 4.1 Density-based pre-filtering

Any sampling method faces an inherent trade-off: reducing the size of the data as much as possible while maintaining a good classification accuracy on the sample. One can frame this as an optimization problem

where *diff* is a similarity between two decision functions and \(\varepsilon\) a tolerable deterioration in accuracy. Solving Optimization Problem 4 requires knowledge of \(f^\mathbf {X}\). But obtaining this knowledge is infeasible. The reason is that \(\vert \mathbf {X} \vert\) is too large to solve — SVDD would not need any sampling in the first place otherwise. Thus, one cannot infer which observations \(f^\mathbf {X}\) classify as inlier or outlier. However, we know that the SVDD hyperparameter *C* defines a lower bound on the share of observations predicted as outliers in the training data (Tax and Duin 2004). A special case is if \(C=1\), since \(f^\mathbf {X}(x;\ C\!=\!1) = \text {in}, \forall x \in \mathbf {X}\). Recall that this is the upper bound of the cost parameter *C* where SVDD degenerates to a hard-margin classifier, cf. Sect. 2. In this case, *diff* is zero if SVDD trained on \(\mathbf {S}\), i.e., \(f^\mathbf {S}\), also includes all observations within the hypersphere. Further, we can make use of the following characteristic of SVDD.

### Characteristic 1

*(SVDD Level-Set Estimator) SVDD is a consistent level set estimator for the Gaussian kernel* (Vert and Vert 2006).

In consequence, inliers form a super-level set with respect to the decision boundary. Formally, this means that there exists a level set \(L_\theta\) and a corresponding level-set classifier \(g^\mathbf {X}_\theta\) such that \(g^\mathbf {X}_\theta \equiv f^\mathbf {X}\). We can exploit this characteristic as follows. First, we *pre-filter* the data based on their empirical density, such that a share of \(p_\text {out}\) observations are outliers. Formally, \(p_\text {out}\) is equivalent to choosing a threshold \(\theta _\text {pre}\) on the empirical density, where \(\theta _\text {pre}\) is the \(p_\text {out}\)-th quantile of the empirical density distribution. Using this threshold in a level-set classifier separates observations into inliers \(\mathbf {I}\) and outliers \(\mathbf {O}\).

Second, we replace \(f^\mathbf {X}\) with \(f^\mathbf {I}\) and set \(C=1\). With this, we know that \(f^\mathbf {I}(x) = \text {in}, \forall x \in \mathbf {I}\), without training \(f^\mathbf {I}\). Put differently, pre-filtering the data with an explicit threshold allows to get rid of an implicit outlier threshold *C*. This in turn allows to estimate the level set estimated by SVDD without actually training the classifier. Algorithm 1 is the pseudo code for the pre-filtering.

Pre-filtering does not add any new exogenous parameter, but replaces the SVDD trade-off parameter *C* with \(p_\text {out}\). Further, \(p_{out}\) is a parameter of SVDD, not of our sampling method. We also deem \(p_\text {out}\) slightly more intuitive than *C*, since it makes the lower bound defined by *C* tight, i.e., pre-filtering assumes an exact outlier ratio of \(p_\text {out} = {|\mathbf {O}|}/{|\mathbf {X}|}\). This in turn makes the behavior of SVDD more predictable. We note further that in an unsupervised case the *C* parameter of the SVDD is commonly coupled with the “target error estimate” introduced in Tax and Duin (2004): The “target error estimate” is exactly the expected outlier percentage \(p_\text {out}\), and one sets \(C \le 1 / (N * p_\text {out})\). So our pre-filtering step uses exactly the \(p_\text {out}\) estimate that one would use for parametrization of SVDD in an unsupervised scenario. We close the discussion of pre-filtering with two remarks.

###
*Remark 1*

Technically, one may directly use the level-set classifier \(g_{\theta _\text {pre}}^{\mathbf {X}}\) instead of SVDD. However, classification times are very high, since calculating the kernel density of an unseen observation is in \(\mathcal {O}(N)\). So one would give up fast classification, one of the main benefits of SVDD. Next, one may be tempted to interpret this pre-filtering step as a way to transform an unsupervised problem into a supervised one to train a binary classifier (e.g., SVM) on \(\mathbf {O}\) and \(\mathbf {I}\). However, binary classification assumes the training data to be representative of the underlying distributions. This assumption is not met with outlier detection, since outliers may not come from a well-defined distribution. Thus, binary classification is not applicable.

###
*Remark 2*

Pre-filtering is a necessary step with all sampling methods discussed in related work. In Sect. 3, we have explained that existing sampling methods assume to only have inliers in the data set, i.e., \(\mathbf {I} = \mathbf {X}\) and \(\mathbf {O} = \emptyset\). However, if \(\mathbf {X}\) contains outliers, this affects the sampling quality negatively and leads to poor SVDD results, see Sect. 5.3.

### 4.2 Optimal sample selection

After *pre-filtering*, we can reduce Optimization Problem 4 to a feasible optimization problem. We begin by replacing \(f^\mathbf {X}\) with \(f^\mathbf {I}\). With Characteristic 1, we further know that both classifiers have equivalent level-set classifiers. We set \(g^{\mathbf {I}}_{\theta _\text {pre}}\) as the equivalent level-set classifier for \(f^\mathbf {I}\). For \(f^\mathbf {S}\), there also exists a level-set classifier \(g^{\mathbf {S}}_{\theta '}\), but the level set \(\theta '\) depends on the choice of \(\mathbf {S}\). Thus, we must additionally ensure that \(\theta '\) indeed is the level set estimated by training SVDD on \(\mathbf {S}\). The modified optimization problem is

where \(\equiv\) denotes the equivalence in classifying \(\mathbf {S}\). Constraint 5b is necessary, since one may select a sample that yields a level-set classifier similar to the one obtained from \(\mathbf {I}\), but on which SVDD returns another decision boundary. This can, for instance, occur if \(\mathbf {S}\) does not contain the boundary points of \(\mathbf {I}\). Optimization Problem 5 still is very abstract. We will now elaborate on both of its constraints and show how to reduce them so that the problem becomes practically solvable.

*Constraint* 5a We now discuss how to obtain a sample that minimizes \(\textit{diff}(g^{\mathbf {S}}_{\theta '}, g^{\mathbf {I}}_{\theta })\). To this end, we use the following theorem.

###
**Theorem 1**

\(g^\mathbf {S}_{\theta '} \equiv g^\mathbf {I}_{\theta }\) *if* \(d_{\mathbf {S}}\) *is uniform on* \(\mathbf {I}\).

###
*Proof*

Think of a sample \(\mathbf {S} \subseteq \mathbf {I}\) with uniform empirical density \(d_\mathbf {S}\). Then \(\mathbf {S}\) has exactly one level set \(\theta '=\theta _\text {min} = \min _{x \in \mathbf {S}} d_{\mathbf {S}}(x)\). Further, it also holds that \(d_{\mathbf {S}}(x) = \theta _\text {min}\), \(\forall x \in \mathbf {I}\). It follows that \(\min _{x \in \mathbf {I} \setminus \mathbf {S}} d_{\mathbf {S}}(x) = \min _{x \in \mathbf {S}} d_{\mathbf {S}}(x)\), and consequently \(g^\mathbf {S}_{\theta _\text {min}}(x) = g^\mathbf {I}_{\theta }(x), \forall x \in \mathbf {I}\). \(\square\)

Theorem 1 implies that one can satisfy Constraint 5a with \(\varepsilon =0\) if one reduces the sample to one with a uniform empirical distribution \(d_\mathbf {S}\). However, any empirical density estimate on a finite sample can only *approximate* a uniform distribution. So one should strive for solutions of Optimization Problem 5 where epsilon is small. Put differently, one can interpret the difference between a perfect uniform distribution and the empirical density to assess the quality of a sample. We propose to quantify the fit with a uniform distribution as the difference between the maximum density \(\theta _\text {max} = \max _{x \in \mathbf {S}} d_\mathbf {S}(x)\) and minimum density \(\theta _\text {min} = \min _{x \in \mathbf {S}} d_\mathbf {S}(x)\):

There certainly are other ways to evaluate the goodness of fit between distributions. However, \(\Delta ^{\mathbf {S}}_\text {fit}\) has some desirable properties of the sample, which we discuss in Theorem 2.

One further consequence of only approximating a uniform density is that there may be some not-selected observations \(x \in \mathbf {I}\setminus \mathbf {S}\) with a density value \(d_{\mathbf {S}}(x)\) less than \(\theta _\text {min}\). Since the level set estimated by \(f^\mathbf {S}\) is \(L_{\theta _\text {min}}\), these not-selected observations would be wrongly classified as outliers. Thus, we must also ensure that \(\mathbf {S}\) is selected so that \(d_{\mathbf {S}}(x) \ge \theta _\text {min}, \forall x \in \mathbf {I}\setminus \mathbf {S}\). We can now re-formulate Constraint 5a as a sample optimization problem SOP.

where \(\mathcal {I} = \{i \ \vert \ i \in \{1, \dots , N\}, x_i \in \mathbf {I}\}\), \(\mathcal {O}=\{1, \dots , N\} \setminus \mathcal {I}\). The decision variable \(v_j=1\) indicates if an observation \(x_j\) is in \(\mathbf {S}\), i.e., \(\mathbf {S} = \{x_i \in \mathbf {X} \ \vert \ v_i = 1\}\). Constraint 7b is a technical necessity to obtain the maximum density of \(d_\mathbf {S}\). The first constraint in 7d rules out the trivial solution \(v = \mathbf{0}\). The first constraint in 7e results from the *pre-filtering*, cf. Sect. 4.1. If the solution set of SOP is not singular, we select the solution where \(\vert \mathbf {S} \vert\) is minimal to minimize training time.

Constraints 7a, 7c, and 7d together guarantee that the density of not-selected observations is at least \(\theta _\text {min}\), as follows. Only for one observation *j* we have \(w_j = 1\) and for all other observations \(i \ne j, \; w_i = 0\). Then for Constraint 7c and 7d to hold, *j* must be the observation with the minimum density and \(d_\mathbf {S}(x_j) = \theta _\text {min}\). Additionally, with \(v_j \ge w_j\) it follows that \(v_j = 1\), thus observation *j* is in the sample \(\mathbf {S}\). So, for any feasible solution of SOP all not-selected observations have a density of at least the minimum density of the selected observations. From 7a, it follows that \(d_\mathbf {S}(x) \ge \theta _\text {min}, \forall x \in \mathbf {I}\). So any solution of SOP satisfies Inequality 5a with a small \(\varepsilon\).

*Constraint* 5b We now show that a solution of SOP also satisfies Constraint 5b. To this end, we make use of the following characteristic.

### Characteristic 2

(*Boundary Points) The set of boundary points are a superset of the support vectors of SVDD* (Tax and Duin 2004).

So for Constraint 5b to hold, an optimum of SOP must contain boundary points of \(\mathbf {I}\). We show that a solution with boundary points is preferred over one without boundary points by the following theorem.

###
**Theorem 2**

*The set of boundary points does not change when solving SOP iteratively.*

###
*Proof*

Suppose that there exists a sample \(\mathbf {S}\) which is not a local optimum of SOP. Then there is a boundary point \(x_{\text {min}} = {{\,\mathrm{arg\,min}\,}}_{x \in \mathbf {S}} d_{\mathbf {S}}(x)\), an observations \(x_{\text {max}} = {{\,\mathrm{arg\,max}\,}}_{x \in \mathbf {S}} d_{\mathbf {S}}(x)\) and \(x_p \in \mathbf {S}\). Let \(\mathbf {S}_p = \mathbf {S} \! \setminus \! \{x_p\}\) and \(\mathbf {S}_{\text {max}} = \mathbf {S} \! \setminus \! \{x_{\text {max}}\}\). If removing \(x_p\) from \(\mathbf {S}\) is an optimal choice, there must be no other observation that reduces the objective more than \(x_p\). Thus, the following specific case must hold:

For one, we conclude that \(x_p = x_\text {min}\) is not feasible, because in this case the left hand side of Inequality 8 is strictly negative, and right hand side positive. Since boundary points have, per Definition 2, a density close to \(\theta _{\text {min}}\), they cannot be a candidate for removal.

Next, under two assumptions that (A1) the locations of the maximum and of the minimum density are distant from each other, and that (A2) the kernel bandwidth is sufficiently small, we have \(k(x_\text {max}, x_\text {min})~\rightarrow ~0\), and \(k(x_p, x_\text {max}) - k(x_p, x_\text {min}) \ge 1 \Leftrightarrow x_p = x_\text {max}\). So in this case, removing \(x_\text {max}\) is optimal. From this, it also follows that the minimum density does not change significantly when removing \(x_\text {max}\). With Definition 2, it follows that also the set of boundary points does not change after removing \(x_\text {max}\). \(\square\)

###
*Remark 3*

Our proof hinges on two assumptions: *(A1) A sufficiently large distance between* \(x_\text {max}\) * and * \(x_\text {min}\). This assumption is intuitive, since removing an observation with a density close to \(\max _{x \in \mathbf {S}} d_{\mathbf {S}}(x)\) improves \(\Delta _\text {fit}\) more than removing one close to \(\min _{x \in \mathbf {S}} d_{\mathbf {S}}(x)\). Generally, the distance between \(x_\text {max}\) and \(x_\text {min}\) depends on the data distribution. However, we find that this is not a limitation in practice, see Sect. 5. *(A2) A sufficiently small kernel bandwidth.* This assumption is reasonable, because when selecting the kernel bandwidth, one strives to avoid underfitting, i.e., avoid kernels bandwidth that are too wide. This holds empirically as well, see Sect. 5.

###
*Remark 4*

Overfitting the kernel parameter of SVDD affects all sampling methods. When the kernel bandwidth is very small, removing any observations from a sample yields a decision boundary that is different from the one obtained with training on the full data set. For SOP an overfitted kernel bandwidth results in density values of approximately 1 for all observations with the Gaussian kernel, i.e., the density is already uniform. The objective function of SOP then is already minimal, with a value of 0. Thus, SOP does not remove any observation from the sample and retains the original decision boundary. In practice, one can rely on one of the many heuristics to choose a suitable kernel parameter to avoid overfitting, see for example our choice in Sect. 5.

SOP is appealing in theory. However, it is a mixed-integer problem with non-convex constraints, and it is hard to solve. Thus, solver runtimes quickly become prohibitive, even for relatively small problem instances. This contradicts the motivation for sampling. We therefore propose RAPID, a fast algorithm to search for a local optimum of SOP.

### 4.3 A RAPID approximation

The idea of our approximation is to initialize \(\mathbf {S} = \mathbf {I}\), which is a feasible solution to SOP, and remove observations from \(\mathbf {S}\) iteratively as long as \(\mathbf {S}\) remains feasible, see Algorithm 2. RAPID is a fast greedy algorithm, i.e., it may not produce the smallest sample with uniformity, cf. objective function of SOP. However, the proofs for SOP that sampling retains the decision boundary also hold for RAPID.

As input parameters RAPID takes the data set \(\mathbf {X}\), the expected outlier percentage \(p_\text {out}\) and a kernel function *k*. Line 1 is the *pre-filtering*. RAPID then iteratively selects the most dense observation \(x_\text {max}\) in the current sample \(\mathbf {S}\) for removal (Line 3) and updates the densities (Line 4). If \(\mathbf {S}\setminus \{x_\text {max}\}\) is infeasible, RAPID terminates (Line 5–7). Line 6 checks whether there is an observation \(x_i \in \mathbf {I}\) that violates Constraint 7a. As required by SOP, RAPID does not remove boundary points. This is because \(x_\text {max}\) must not be a boundary point, as long as \(\mathbf {S}\) is not uniform, i.e., \(\Delta ^{\mathbf {S}}_\text {fit} > 0\). Thus, a solution of RAPID satisfies both Constraint 5a and Constraint 5b. The return in Line 11 is the special case where a single observations remains in the sample. In this case uniformity is achieved with one observations, i.e., all observations are equal.

The overall time complexity of RAPID is in \(\mathcal {O}(N^2)\), see Algorithm 1 and Algorithm 2 for the step-wise time complexities. Further, RAPID is simple to implement with only a few lines of code. It is efficient, since each iteration (Line 3–7) requires only one pass over the data set to update the densities, compute the new \(x_\text {max}\), \(\theta _\text {min}\) and minimum inlier density for the termination criterion. One may further pre-compute the Gram matrix \(\mathbf {K}\) for \(\mathbf {X}\) to avoid redundant kernel function evaluations.

###
*Remark 5*

RAPID does not require any hyperparameters in addition to the ones already required by SVDD. The two parameters are: a parametrized kernel function *k* and the outlier percentage \(p_\text {out}\). The outlier percentage \(p_\text {out}\) is commonly estimated to calculate the *C* parameter of SVDD (Tax and Duin 2004). Since we guarantee that RAPID retains the decision boundary one would learn on the full data set, the kernel parametrization affects the sampling. However, due to the density rule, the parametrization only affects how many observations RAPID removes from the sample. Yet RAPID always retains the decision boundary. While the exact sampling always depends on the data set, the general intuition is that, with a higher kernel width, RAPID can remove more observations than with a more narrow one. In the extreme case of a very small kernel width, RAPID cannot remove any observations without violating the density rule, c.f. our discussion in Remark 4. Ultimately, given a novel data set, one must set the same parameters for SVDD with or without sampling with RAPID. One commonly relies on one of the many heuristics to parametrize SVDD, see our discussion at the end of Sect. 2.

## 5 Experiments

We now turn to an empirical evaluation of RAPID. Our evaluation consists of two parts. In the first part, we evaluate how well RAPID copes with different characteristics of the data, i.e., with the dimensionality, the number of observations, and the complexity of the data distribution, see Sect. 5.2. The second part is an evaluation on a large real-world benchmark for outlier detection. We have implemented RAPID as well as the competitors in an open-source framework written in Julia (Bezanson et al. 2017). Our implementation, data sets, raw results, and evaluation notebooks are publicly available.^{Footnote 1}

### 5.1 Setup

We first introduce our experimental setup, including evaluation metrics, as well as the parametrization of SVDD and its competitors. Recall that RAPID does not have any exogenous parameter. One must only specify \(p_\text {out}\) instead of the SVDD hyperparameter *C*, cf. Sect. 4.1.

*Metrics* Sampling methods trade classification quality for sample size, and one must evaluate this trade-off explicitly. We report the sample size \(\vert \mathbf {S}\vert\) and sample ratio \({\vert \mathbf {S}\vert }/{\vert \mathbf {X} \vert }\) for each result. To evaluate the classification quality, we use the Matthews Correlation Coefficient (MCC) on \(\mathbf {X}\). MCC is well-suited for imbalanced data and returns values in \([-1, 1]\); higher values are better. SVDD returns a binary classification which is different from many other outlier-detection methods which produce score-based outputs (Aggarwal 2015b). For such score-based outputs, one usually calculates ROC-AUC. ROC-AUC and MCC are statistically consistent with each other (Halimu et al. 2019), we report the values for other evaluation metrics (ROC-AUC, F1-score and Cohen’s kappa coefficient) in the appendix of this article. For a full analysis see our supplementary material. We report the averages over five runs on synthetic data and perform 5-fold cross-validation on real-world data. For non-deterministic methods, we report average values over five repetitions. Our experiments ran on an AMD Ryzen Threadripper 2990WX with 64 virtual cores and 128 GB RAM.

*SVDD* SVDD requires to set two hyperparameters: the Gaussian kernel parameter \(\gamma\) and the trade-off parameter *C*. We tune \(\gamma\) with *Scott’s Rule* (Scott 2015) for real-world data. For high-dimensional synthetic data, however, we found that the *Modified Mean Criterion* (Liao et al. 2018) is a better choice. The *Modified Mean Criterion* in these cases yields a higher kernel bandwidth. This allows sampling to remove more observations, c.f. Remark 5. Because of *pre-filtering* we set \(C=1\), cf. Sect. 4.1.

*Competitors* We compare our method against 8 competitors, see Table 1. The approaches from Qu et al. (2019) and Krawczyk et al. (2019) require to solve several hundreds of SVDDs, resulting in prohibitive runtimes. We do not include them in our evaluation. We initialize the exogenous parameters according to the guidelines in the original publications. In some cases, the recommendations do not lead to a useful sample, e.g., \(\mathbf {S} = \emptyset\). To ensure a fair comparison, we mitigate these issues by fine-tuning the parameter values through preliminary experiments.

Next, we compare two variants of each competitor: sampling on \(\mathbf {X}\) as in their original version, and sampling on \(\mathbf {I}\), i.e., after applying our *pre-filtering*. The *pre-filtering* requires to specify the expected outlier percentage \(p_\text {out}\). In practice, one can rely on domain knowledge or estimate it Achtert et al. (2010). To avoid any bias when over- or under-estimating the outlier percentage, we set it to the true percentage. Nevertheless, we have run additional experiments where we deliberately deviate from the true percentage. We found that deviating affects the performance of all sampling methods similarly. So, our conclusions do not depend on this variation, and we report the respective results only in the supplementary materials.\(^1\)

We also evaluate against random baselines. Each baseline \(\textit{Rand}_\textit{r}\) returns a random subset with a specified sample ratio *r*. We report results for a range of sample ratios \(r \in [0.01, 1.0]\) to put the quality of competitors into perspective. When choosing the *C* parameter of SVDD for the random baseline, one must observe that outliers may be part of the selected sample. However, in experiments of ours, we have observed that \(C=1\) generally yields the most competitive baseline even if some outliers are part of the training data. Training a \(r=1\) baseline on the full data set is prohibitive for large data sets. So we only report the values for the smaller data sets.

### 5.2 Evaluation of sample characteristics

The first part of our experiments validates different properties of RAPID and of its competitors. Our intention is to give an intuition of how a sample is selected, and to explore under which conditions the sampling methods work well. The basis for our experiments are synthetic data sets with controlled characteristics. Specifically, we generate data from Gaussian mixtures with varying number of mixture components, data dimensions, and number of observations, see Algorithm 3 for the data generation algorithm. We run these experiments to answer the following two questions.

**Q1** How are observations in a sample distributed?

To get an intuition about the sample distribution, we run RAPID and the competitors on a bi-modal Gaussian mixture, see Fig. 4. The tendencies of the methods to select boundary points and inner points are clearly visible. For instance, BPS only selects a sparse set of boundary points; IESRSVDD only prunes high-density areas. As expected, RAPID selects both the boundary points and a uniformly distributed set of inner points. The decision boundary of RAPID matches the one obtained from the full data set perfectly. Only three competitors (DAEDS, IESRSVDD, and NDPSR) also result in an accurate decision boundary. But all of them produce significantly larger sample sizes than RAPID.

**Q2** To what extent do data characteristics influence a sample and the resulting classification quality?

To explore this question, we individually vary the number of observations, the dimensionality, and the number of the mixture components. In the following visualizations, an optimal sampling always yields a MCC of 1 in the upper row and very small sample sizes in the bottom row, i.e., altering any data characteristic does not influence the sampling. Some values for the competitors are missing since the sample has been empty.

*Number of observations* Ceteris paribus, increasing the number of observations should not have a significant impact on the observations selected. This expectation is reasonable, since increasing the data size does not change the underlying distribution and the true decision boundary. Figure 5a graphs the sample quality and sample size for the different methods. Many competitors (BPS, IESRSVDD, KFNCB, and DAEDS) do not scale well with more observations, i.e., the sample sizes increase significantly. BPS scales worst and only removes a tiny fraction of observations. Further, the sample quality drops significantly with more than 500 observations for some competitors (DBSRSVDD and HSR). RAPID on the other hand is robust with increasing data size, for both sample quality and sample size. The sample sizes returned are small, even for large data sets, and the resulting quality is always close to MCC = 1.0.

*Dimensionality* The expectation is that the sample quality does not deteriorate with increasing dimensionality. However, sample sizes may increase slightly. This is because determining a decision boundary of a high-dimensional manifold requires more observations than of a low-dimensional one. Figure 5b shows the sample quality and size. For some competitors (HSR, NDPSR, and KFNCBD), sample quality decreases with increasing dimensionality. This indicates that they do not select observations in all regions. This in turn leads to misclassification. Even tuning exogenous parameter values does not mitigate these effects. As desired, RAPID returns a small sample in all cases, with high classification accuracy.

*Number of Mixture Components* Finally, we make the data set more difficult by increasing the number of Gaussian mixture components. Like before, we expect sample sizes to increase slightly, since the generated manifolds are more difficult to classify. Figure 5c shows the sample quality and size. For HSR and DBSRSVDD, sampling quality fluctuates significantly. NDPSR and DBSRSVDD do not prune any observation with only one component. We think that these effects are due to the sensitivity to the exogenous parameters of the various methods. This is, methods with fluctuating results would require different parameter values for data sets of different difficulties. However, the competitors do not come with a systematic way to choose parameter values to adapt to varying data set difficulty. RAPID in turn is very robust to changes in difficulty. As expected, the sample size increases only slightly with increasing difficulty. The classification accuracy is close to MCC = 1.0, even for high difficulties.

* In summary, our experiments on synthetic data reveal that many competitors are sensitive to data size, dimensionality, and complexity. Different parameter values may mitigate the effects in a few cases, but selecting good values is difficult. RAPID on the other hand is very robust. It adapts well to different data characteristics and does not require any parameter tuning. *
^{Footnote 2}

### 5.3 Benchmark on real-world data

Next, we turn to data sets with real distributions and more diverse data characteristics. The basis for our experiments are 23 standard benchmark data sets for outlier detection (Campos et al. 2016). Campos et al. constructed this benchmark from classification data where one of the classes is downsampled and labeled as outlier. The data sets have different sizes (80 to 49534 observations), dimensionality (3 to 1555 dimensions) and outlier ratios (0.2% to 75.38%, median 9.12%).^{Footnote 3} Again, we structure our experiments along two questions.

**Q3** How well do methods adapt to real-world data sets?

First, we compare RAPID against competitors without any pre-processing. Figure 6 plots the median sample ratio against the SVDD quality over all data sets.\(^2\) Good sampling methods return small sample ratios and yield high SVDD quality, i.e., they appear in the upper right corner of the plot. Rand is shown for different \(r \in [0.01, 1.0]\). All of the competitors in their original version, i.e., without pre-filtering, result in poor SVDD quality, much lower than the Rand baselines. The reason is that they expect all observations to be inliers. BPS with pre-filtering did not yield any solution for large data sets.

With our *pre-filtering*, SVDD qualities of competitors improve considerably, see Fig. 6 and Table 2. Still, RAPID outperforms its competitors; none of them produces a sample with higher SVDD quality or smaller sample size than RAPID. The methods closest to RAPID are IESRSVDD and NDPSR, with similar SVDD quality, but significantly larger sample sizes. On average, the sample selected by RAPID even yields the same quality as training a SVDD without sampling.^{Footnote 4} Figure 7 in the Appendix of this article features a more detailed evaluation per data set.

**Q4** What are the runtime benefits of sampling?

Next, we look at the impact of sampling on algorithm runtimes, see Table 2. We measure the execution runtimes of the sampling method (\(t_\text {samp}\)), of SVDD training on the sample (\(t_\text {train}\)), and of the classification (\(t_\text {class}\)). Overall, all methods have reasonable runtimes for sampling, with DAEDS being the slowest with 0.35s on average. However, RAPID is the fastest method overall Methods with runtimes similar to RAPID, such as DBSRSVDD, feature significantly lower SVDD quality. For the big data sets (ALOI and KDDCup99), RAPID, DBSRSVDD, FBPE, and HSR have a \(t_\text {samp}\) of around one minute or less, see Fig. 8 and Table 3 in the Appendix of this article. RAPID achieves the highest classification quality nevertheless, even compared to the slower competitors. Compared to SVDD applied to large original data sets without sampling, RAPID reduces training times from over one hour to only a few seconds.\(^4\)

Finally, we look at the statistical significance of our results. We perform a Friedman test with a pairwise comparison of the methods via a post-hoc Neményi test for three metrics: SVDD quality (MCC), sample ratio (\({\vert \mathbf {S}\vert }/{\vert \mathbf {X}\vert }\)) and algorithm runtime \(t_\text {samp}\). The test on SVDD quality confirms that no other method is significantly better than RAPID. Yet RAPID produces significantly smaller samples (\(p < 0.01\) for all competitors except for FBPE where \(p < 0.05\)). RAPID also is significantly faster at sampling the data set than BPS, DAEDS, DBSRSVDD, KFNCBD, and NDPSR, the closest competitor in terms of quality (\(p < 0.01\)). For more details see Figs. 9, 10 and 11 in the Appendix of the article.

* In summary, RAPID outperforms its competitors on real-world data as well. There is no other method with higher SVDD quality and similarly small sample sizes. RAPID scales very well to very large data sets and reduces overall runtimes by up to an order of magnitude. *

## 6 Conclusions

SVDD does not scale well to large data sets due to long training runtimes. Therefore, working with a sample instead of the original data has received much attention in the literature. Various existing sampling approaches guess the support vectors of the original SVDD solution from data characteristics. These methods are difficult to calibrate because of unintuitive exogenous parameters. They also tend to perform poorly regarding outlier detection. One reason is that including support vector candidates in the sample does not guarantee them to indeed become support vectors.

Our article addresses these issues. We formalize SVDD sample selection as an optimization problem, where constraints guarantee that SVDD indeed yields the correct decision boundaries. We achieve this by reducing SVDD to a density-based decision problem, which gives way to rigorous arguments why a sample indeed retains the decision boundary. To solve this problem effectively, we propose a novel iterative algorithm RAPID. RAPID does not rely on any parameter tuning beyond the one already required by SVDD. It is efficient and consistently produces a small high-quality sample. Experiments show that the way we have framed sampling as an optimization problem improves substantially on existing methods with respect to runtimes, sample sizes, and classification accuracy.

## Data availability statement

All data and material used in this article is available on our website(https://www.ipd.kit.edu/ocs). The website contains links to our raw results, figures and tables. The real-world data sets used in Sect. 5.3 were published in Campos et al. (2016) and are available on their website (https://www.dbs.ifi.lmu.de/research/outlier-evaluation/DAMI/).

## Code availability

All code used for reproducing our experiments, evaluation, and plotting for this article is open source, MIT licensed and available on Github (https://github.com/englhardt/ocs-evaluation/).

## Notes

Because of limited space, we report median statistics, but results also hold for mean values and individual comparisons (ranks), see https://www.ipd.kit.edu/ocs.

Only the data set “Parkinson” has an outlier percentage higher than 40%.

Based on data sets with non-prohibitive runtime, i.e., \(N < {25000}\), see https://www.ipd.kit.edu/ocs for details.

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All authors contributed to the algorithm conception and design. Experimental evaluation was performed by Adrian Englhardt. The first draft of the manuscript was written by Adrian Englhardt and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Englhardt, A., Trittenbach, H., Kottke, D. *et al.* Efficient SVDD sampling with approximation guarantees for the decision boundary.
*Mach Learn* **111**, 1349–1375 (2022). https://doi.org/10.1007/s10994-022-06149-0

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DOI: https://doi.org/10.1007/s10994-022-06149-0