Approximate information discriminant analysis: A computationally simple heteroscedastic feature extraction technique

Authors:
Koel Das;Zoran Nenadic
Affiliations:
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA;Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
Venue:
Pattern Recognition
Year:
2008

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Abstract

In this article we develop a novel linear dimensionality reduction technique for classification. The technique utilizes the first two statistical moments of data and retains the computational simplicity, characteristic of second-order techniques, such as linear discriminant analysis. Formally, the technique maximizes a criterion that belongs to the class of probability dependence measures, and is naturally defined for multiple classes. The criterion is based on an approximation of an information-theoretic measure and is capable of handling heteroscedastic data. The performance of our method, along with similar feature extraction approaches, is demonstrated based on experimental results with real-world datasets. Our method compares favorably to similar second-order linear dimensionality techniques.