This paper shows the importance of the use of class information in feature extraction for classification and inappropriateness of conventional PCA to feature extraction for classification. We consider two eigenvector-based approaches that take into account the class information. The first approach is parametric and optimizes the ratio of between-class variance to within-class variance of the transformed data. The second approach is a nonparametric modification of the first one based on local calculation of the between-class covariance matrix. We compare the two approaches with each other, with conventional PCA, and with plain nearest neighbor classification without feature extraction.
Published Date: May 2002
Registration: ISBN 978-1-57735-141-2
Copyright: Published by The AAAI Press, Menlo Park, California