Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning sparse features for classification by mixture models
Pattern Recognition Letters
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
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Nonnegative Matrix Factorization (NMF) is a powerful tool forgene expression data analysis as it reduces thousands of genes to afew compact metagenes, especially in clustering gene expressionsamples for cancer class discovery. Enhancing sparseness of thefactorisation can find only a few dominantly coexpressed metagenesand improve the clustering effectiveness. Sparse p-norm (p 1)Nonnegative Matrix Factorization (sp-NMF) is a more sparserepresentation method using high order norm to normalise thedecomposed components. In this paper, we investigate the benefit ofhigh order normalisation for clustering cancer-related geneexpression samples. Experimental results demonstrate that sp-NMFleads to robust and effective clustering in both automaticallydetermining the cluster number, and achieving high accuracy.