【文件属性】:
文件名称:Non-negative Local Coordinate Factorization for Image Representation
文件大小:486KB
文件格式:PDF
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.