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	Automatic Non-negative Matrix Factorization Clustering with Competitive Sparseness Constraints
	Liu, Chenglin ; Ma, Jinwen
	2014
关键词	Non-negative matrix factorization Competitive learning Clustering analysis LEAST-SQUARES
英文摘要	Determination of the appropriate number of clusters is a big challenge for the bi-clustering method of the non-negative matrix factorization (NMF). The conventional determination method may be to test a number of candidates and select the optimal one with the best clustering performance. However, such strategy of repetition test is obviously time-consuming. In this paper, we propose a novel efficient algorithm called the automatic NMF clustering method with competitive sparseness constraints (autoNMF) which can perform the reasonable clustering without pre-assigning the exact number of clusters. It is demonstrated by the experiments that the autoNMF has been significantly improved on both clustering performance and computational efficiency.; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; EI; CPCI-S(ISTP); 0
语种	英语
出处	EI ; SCI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/405633]
专题	数学科学学院
推荐引用方式 GB/T 7714	Liu, Chenglin,Ma, Jinwen. Automatic Non-negative Matrix Factorization Clustering with Competitive Sparseness Constraints. 2014-01-01.

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