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Using Discriminative Dimensionality Reduction to Visualize Classifiers

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Abstract

Albeit automated classifiers offer a standard tool in many application areas, there exists hardly a generic possibility to directly inspect their behavior, which goes beyond the mere classification of (sets of) data points. In this contribution, we propose a general framework how to visualize a given classifier and its behavior as concerns a given data set in two dimensions. More specifically, we use modern nonlinear dimensionality reduction (DR) techniques to project a given set of data points and their relation to the classification decision boundaries. Furthermore, since data are usually intrinsically more than two-dimensional and hence cannot be projected to two dimensions without information loss, we propose to use discriminative DR methods which shape the projection according to given class labeling as is the case for a classification setting. With a given data set, this framework can be used to visualize any trained classifier which provides a probability or certainty of the classification together with the predicted class label. We demonstrate the suitability of the framework in the context of different dimensionality reduction techniques, in the context of different attention foci as concerns the visualization, and as concerns different classifiers which should be visualized.

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Notes

  1. We use the estimator \(\hat{h}_{rot}\) provided in the literature to specify this parameter, see e.g. [36].

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Acknowledgments

Funding from DFG under Grant number HA2719/7-1 and by the CITEC center of excellence is gratefully acknowledged. We also would like to thank the reviewers for many helpful comments and ideas concerning the evaluation.

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Authors and Affiliations

  1. Bielefeld University, Bielefeld, Germany

    Alexander Schulz, Andrej Gisbrecht & Barbara Hammer

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  1. Alexander Schulz
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  2. Andrej Gisbrecht
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  3. Barbara Hammer
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Correspondence to Alexander Schulz.

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Schulz, A., Gisbrecht, A. & Hammer, B. Using Discriminative Dimensionality Reduction to Visualize Classifiers. Neural Process Lett 42, 27–54 (2015). https://doi.org/10.1007/s11063-014-9394-1

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