Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data
by Weixiang Liu, Kehong Yuan
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 2, No. 3, 2008

Abstract: 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 (sp-NMF) is a more sparse representation method using high order norm to normalise the decomposed components. In this paper, we investigate the benefit of high order normalisation 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.

Online publication date: Mon, 29-Sep-2008

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