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In this paper, we propose a framework for approximate NMF which constrains the L3/2 norm of the coefficient matrix, called Sparse NMF with Fractional Norm Constraints (NMFFN), which based on the convex and smooth L3/2 norm. When original data is factorized in lower dimensional space using NMF, NMFFN uses the convex and smooth L3/2 norm as sparse constrain for the low dimensional feature. An efficient...
Matrix factorization techniques have been frequently applied in data representation and pattern recognition. One of them is Concept Factorization (CF), which is a new matrix decomposition technique for data representation. In this paper, we propose a novel semi-supervised matrix factorization algorithm, called Constrained Graph Concept Factorization (CGCF), which incorporates the label information...
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