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Abstract Details
Activity Number:
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356
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #304455 |
Title:
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Sparse Biclustering of Transposable Data Using the Matrix Normal Distribution
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Author(s):
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Kean Ming Tan*+ and Daniela Witten
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Companies:
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University of Washington and University of Washington
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Address:
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Department of Biostatistics, Seattle, WA, , United States
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Keywords:
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biclustering ;
matrix-variate normal distribution ;
sparsity ;
clustering ;
unsupervised learning ;
gene expression
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Abstract:
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We consider the problem of simultaneously clustering both the rows and the columns of a large transposable data matrix. A number of approaches for biclustering have been proposed in recent years, but these proposals have tended to be somewhat ad hoc. Here, we develop a rigorous statistical approach for biclustering that is motivated by the matrix-variate normal distribution. We perform biclustering by maximizing the log likelihood of the data under the assumption that it follows a matrix-variate normal distribution; we further assume that the overall mean structure of the data is sparse, in order to obtain sparse biclustering using a penalized log likelihood approach. Furthermore, we show that k-means clustering of the rows and of the columns of the data matrix can be seen as a special case of our proposal, and that a relaxation of our proposal yields the singular value decomposition. We demonstrate the performance of our sparse biclustering method relative to existing approaches in a simulation study and on two gene expression data sets.
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