文件名称：Non-negative Local Coordinate Factorization for Image Representation
文件大小：486KB
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更新时间：2015-12-05 03:46:10
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Recently Non-negative Matrix Factorization (NMF) has become increasingly popular for feature extraction in com- puter vision and pattern recognition. NMF seeks for two non-negative matrices whose product can best approximate the original matrix. The non-negativity constraints lead to sparse, parts-based representations which can be more ro- bust than non-sparse, global features. To obtain more ac- curate control over the sparseness, in this paper, we pro- pose a novel method called Non-negative Local Coordinate Factorization (NLCF) for feature extraction.