H. Kim and H. Park (USA)
Pattern Recognition, Non-negative Dimension Reduction, Non-negative Matrix Factorization, Sparse NMF
Many practical pattern recognition problems require non negativity constraints. For example, pixels in digital im ages and chemical concentrations in bioinformatics are non-negative. Non-negative matrix factorization (NMF) is a useful technique in approximating these high dimen sional data. Sparse NMFs are also useful when we need to control the degree of sparseness in non-negative basis vec tors or non-negative lower-dimensional representations. In this paper, we introduce novel sparse NMFs via alternat ing non-negativity-constrained least squares. We applied one of the proposed sparse NMFs to cancer class discov ery and gene expression data analysis. Our experimental results illustrate that our proposed method achieves better clustering performance than NMF based on multiplicative update rules and sparse NMFs based on the gradient de scent method.
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