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Backtransformation: a new representation of data processing chains with a scalar decision function

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Data processing often transforms a complex signal using a set of different preprocessing algorithms to a single value as the outcome of a final decision function. Still, it is challenging to understand and visualize the interplay between the algorithms performing this transformation. Especially when dimensionality reduction is used, the original data structure (e.g., spatio-temporal information) is hidden from subsequent algorithms. To tackle this problem, we introduce the backtransformation concept suggesting to look at the combination of algorithms as one transformation which maps the original input signal to a single value. Therefore, it takes the derivative of the final decision function and transforms it back through the previous processing steps via backward iteration and the chain rule. The resulting derivative of the composed decision function in the sample of interest represents the complete decision process. Using it for visualizations might improve the understanding of the process. Often, it is possible to construct a feasible processing chain with affine mappings which simplifies the calculation for the backtransformation and the interpretation of the result a lot. In this case, the affine backtransformation provides the complete parameterization of the processing chain. This article introduces the theory, provides implementation guidelines, and presents three application examples.

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  1. Further methods are presented but they are tailored to functional magnetic resonance imaging (fMRI) data.

  2. The respective derivatives are constant for every sample and as such not depending on it.

  3. The notation of data and its components differs from the notation in classification tasks. Here, we look at one data sample \(x^{(0)}\) with its different processing stages \(x^{(l)}\) and the respective changes in each component of the data \({\left( x^{(l)}_{gh}\right) }\). The double index notation is applied to account for different axes in the data as in time series (different sensors and time points) or images.

  4. With \(n_{k+1}:=1\) it holds that \(\frac{\partial F_l}{\partial y^{(l)}}\in \mathbb {R}^{n_l\times n_{l+1}}\) and the dimensions of \(B_l\) are a consequence of the recursion. Another reason for the dimensions of \(B_l\) is that \(B_l\) corresponds to the mapping of \(x^{(l)}\) to the scalar output \(x^{\text {out}}\).

  5. Note that no matrix inversion is required even though one might expect that, because the goal is to find out what the original mapping was doing with the data which sounds like an inverse approach.

  6. A weighted sum of classifiers preserves linearity/differentiability. A majority vote will result in a non-differentiable classifier but when the score is the sum of the voters for the selected class, the resulting function will still be locally linear/differentiable.


  8. Nevertheless, the resulting graphics look reasonable.

  9. A standard extended 10–20 electrode layout has been chosen with 128 electrodes:


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The authors thank David Feess, Marc Tabie, Anett Seeland, Frank Kirchner, Su Kyoung Kim, Hendrik Wöhrle, and Bertold Bongardt for highly valuable discussions and input. This work was supported by the German Federal Ministry of Economics and Technology (BMWi, Grants FKZ 50 RA 1012 and FKZ 50 RA 1011).

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Correspondence to Mario Michael Krell.

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Krell, M.M., Straube, S. Backtransformation: a new representation of data processing chains with a scalar decision function. Adv Data Anal Classif 11, 415–439 (2017).

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