Recent advances in discriminant non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of pattern recognition applications. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its salient parts has been proposed. An extension of this work specialized for classification, optimized using projected gradients in order to ensure converge to a stationary limit point, resulted in a more efficient method of the latter approach. Assuming multimodality of the underlying data samples distribution and incorporating clustering discriminant inspired constraints into the NMF decomposition cost function, resulted in the Subclass Discriminant NMF algorithm which found to outperform both approaches under real life settings. In this work we review all these methods in the context of various pattern recognition problems using facial images.