期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 23, 期 4, 页码 985-1008出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2013.852554
关键词
l(1) penalty; Gene expression; Unsupervised learning; Matrix-variate normal distribution; Clustering
资金
- NIH [DP5OD009145]
- NSF CAREER [DMS-1252624]
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log-likelihood. We apply an l(1) penalty to the means of the biclusters to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression dataset. This article has supplementary material online.
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