Nonnegative Matrix Factorization on Orthogonal Subspace with Smoothed L0 Norm Constrained

It is known that the sparseness of the factor matrices by Nonnegative Matrix Factorization can influence the clustering performance. In order to improve the ability of the sparse representations of the NMF, we proposed the new algorithm for Nonnegatie Matrix Factorization, coined nonnegative matrix factorization on orthogonal subspace with smoothed L0 norm constrained, in which the generation of orthogonal factor matrices with smoothed L0 norm constrained are the parts of objective function minimization. Also we develop simple multiplicative updates for our proposed method. Experiment on three real-world databases (Iris, UCI, ORL) show that our proposed method can achieve the best or close to the best in clustering and in the way of the sparse representation than other methods.