In this paper, we propose a novel scheme to supervised nonnegative matrix factorization (NMF). We formulate the supervised NMF as a sparse optimization problem assuming the availability of a set of basis vectors, some of which are irrelevant to a given matrix to be decomposed. The number of basis vectors to be actively used is obtained as a consequence of optimization. We present a state-of-the-art convex-analytic iterative solver which ensures global convergence. Simulation results show the efficacy of the proposed scheme in the case of perfect basis matrix.
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