Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiplicative updates for non-negative projections
Neurocomputing
A fast approach for overcomplete sparse decomposition based on smoothed l0 norm
IEEE Transactions on Signal Processing
Nonnegative Matrix Factorization on Orthogonal Subspace
Pattern Recognition Letters
Linear and nonlinear projective nonnegative matrix factorization
IEEE Transactions on Neural Networks
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
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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.