Classification and feature extraction by simplexization

Abstract

Techniques for classification and feature extraction are often intertwined. In this paper, we contribute to these two aspects via the shared philosophy of simplexizing the sample set. For general classification, we present a new criteria based on the concept of k-nearest-neighbor simplex (kNNS), which is constructed by the k nearest neighbors, to determine the class label of a new datum. For feature extraction, we develop a novel subspace learning algorithm, called discriminant simplex analysis (DSA), in which the intraclass compactness and interclass separability are both measured by kNNS distances. Comprehensive experiments on face recognition and lipreading validate the effectiveness of the DSA as well as the kNNS-based classification approach. © 2008 IEEE.