Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Distance metric learning for content identification
IEEE Transactions on Information Forensics and Security
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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 -nearest-neighbor simplex (), which is constructed by the 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 distances. Comprehensive experiments on face recognition and lipreading validate the effectiveness of the DSA as well as the -based classification approach.