Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification

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Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent (2011, methodology): “Here, we evaluate the performance of the proposed MD-NMF comparing with six representative algorithms, which are NMF [24], PCA [18], LNMF [26], GNMF [12], DNMF [47], and NGE [44], on four popular face image datasets including YALE [6], ORL [36], UMIST [19], and CMU PIE [19].”
Online Nonnegative Matrix Factorization With Robust Stochastic Approximation (2012, background): “Recently, several NMF-based dimension reduction algorithms have been proposed [2]–[6] and a unified framework [7] has been proposed to intrinsically understand them.”
A Deep Matrix Factorization Method for Learning Attribute Representations (2015, background): “It has been shown that they can be a successful dimensionality reduction technique over a variety of areas including, but not limited to, environ-metrics [1], microarray data analysis [2], [3], document clustering [4], face recognition [5], [6], blind audio source separation [7] and more.”
Nonnegative Matrix Factorization: A Comprehensive Review (2013, background): “It is to be remarked that the definitions of the within-class and between-class scatters in [97] and [20] are both solely based on the coefficient matrix V , having noting to do with X or U .”