Sequential coordinate-wise DNMF for face recognition

Authors:
Rafal Zdunek;Andrzej Cichocki
Affiliations:
Institute of Telecommunications, Teleinformatics and Acoustics, Wroclaw University of Technology, Wroclaw, Poland;Laboratory for Advanced Brain Signal Processing, RIKEN BSI, Wako-shi, Japan and Warsaw University of Technology, Poland and Systems Research Institute, Polish Academy of Science PAN, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

This paper proposes the Sequential Coordinate-Wise Algorithm (SCWA) to Discriminant Nonnegative Matrix Factorization (DNMF) for improving face recognition. DNMF incorporates Linear Discriminant Analysis (LDA) into NMF using the multiplicative updating rules that are simple in use but usually require many iterations to converge and they do not guarantee the convergence to a stationary point. The SCWA solves the Quadratic Programming (QP) problemby updating only a single variable at each iterative step, which considerably accelerates the convergence for sequentially projected Least Squares (LS) problems that take place in NMF. Moreover, the limit point of the SCWA is the stationary point. The proposed algorithm is tested for supervised face recognition problems where the facial images are taken from the ORL database.