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	Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data
	Liu, Weixiang ; Yuan, Kehong
	2010-05-11 ; 2010-05-11
关键词	Nonnegative Matrix Factorization clustering analysis gene expression data NMF p-norm sparseness data mining bioinformatics CLASS DISCOVERY MIXTURE-MODELS CLASSIFICATION PREDICTION CANCER IMAGES Mathematical & Computational Biology
中文摘要	Nonnegative Matrix Factorization (NMF) is a powerful tool for gene expression data analysis as it reduces thousands of genes to a few compact metagenes, especially in clustering gene expression samples for cancer class discovery. Enhancing sparseness of the factorisation can find only a few dominantly coexpressed metagenes and improve the clustering effectiveness. Sparse p-norm (p > 1) Nonnegative Matrix Factorization (s(p)-NMF) is a more sparse representation method using high order norm to normialise the decomposed components. In this paper, we investigate the benefit of high order normialisation for clustering cancer-related gene expression samples. Experimental results demonstrate that sp-NMF leads to robust and effective clustering in both automatically determining the cluster number, and achieving high accuracy.
语种	英语 ; 英语
出版者	INDERSCIENCE ENTERPRISES LTD ; GENEVA ; WORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 896, CH-1215 GENEVA, SWITZERLAND
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/26397]
专题	清华大学
推荐引用方式 GB/T 7714	Liu, Weixiang,Yuan, Kehong. Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data[J],2010, 2010.
APA	Liu, Weixiang,&Yuan, Kehong.(2010).Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data..
MLA	Liu, Weixiang,et al."Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data".(2010).

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