Journal
PATTERN RECOGNITION
Volume 41, Issue 5, Pages 1548-1557Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2007.10.001
Keywords
feature extraction; information theory; mutual information; entropy; classification; linear discriminant analysis; Bayes error
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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. (c) 2007 Elsevier Ltd. All rights reserved.
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