Logistic Discrimination with Total Variation Regularization

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Logistic Discrimination with Total Variation Regularization
This article introduces a regularized logistic discrimination method that is especially suited for discretized stochastic processes (such as periodograms, spectrograms, EEG curves, etc.). The proposed method penalizes the total variation of the discriminant directions, giving smaller misclassification errors than alternative methods, and smoother and more easily interpretable discriminant directions. The properties of the new method are studied by simulation and by a real-data example involving classification of phonemes. Supported in part by NSF Grant DMS-06-04396. Key words and phrases: Classification, discrimination, machine learning, speech recognition. 1
Robin Rühlicke, Daniel Gervini
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where CSSC
Authors Robin Rühlicke, Daniel Gervini
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