DISCRIMINANT SPARSE NONNEGATIVE MATRIX FACTORIZATION

In this paper, a novel discriminant sparse non-negative matrix factorization (DSNMF) algorithm is proposed. We derive DSNMF method from original NMF algorithm by considering both sparseness constraint and discriminant information constraint. Furthermore, projected gradient method is used to solve the optimization problem. DSNMF makes use of prior class information which is important in classification, so it is a supervised method. Furthermore, by minimization l(1)-norm of the basis, we get a sparse representation of the facial images. Experiments are carried out for facial expression recognition. The experimental results obtained on Cohn-Kanade facial expression database indicate that DSNMF is efficient for facial expression recognition.