A Naive Solution to the One-Class Problem and Its Extension to Kernel Methods
In this work, the problem of estimating high density regions from univariate or multivariate data samples is studied. To be more precise, we estimate minimum volume sets whose probability is specified in advance. This problem arises in outlier detection and cluster analysis, and is strongly related to One-Class Support Vector Machines (SVM). In this paper we propose a new simpler method to solve this problem. We show its properties and introduce a new class of kernels, relating the proposed method to One-Class SVMs.
KeywordsClass Support Vector Machine High Density Region True Mode Naive Algorithm Naive Method
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