Robust integrated locally linear embedding
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Multiple graph regularized nonnegative matrix factorization
Pattern Recognition
Sparse representation for robust abnormality detection in crowded scenes
Pattern Recognition
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Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition tool for multivariate data. Non-negative bases allow strictly additive combinations which have been shown to be part-based as well as relatively sparse. We pursue a discriminative decomposition by coupling NMF objective with a maximum margin classifier, specifically a support vector machine (SVM). Conversely, we propose an NMF based regularizer for SVM. We formulate the joint update equations and propose a new method which identifies the decomposition as well as the classification parameters. We present classification results on synthetic as well as real datasets.