This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 603
Type: Invited
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract - #306114
Title: A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis
Author(s): Daniela Witten*+ and Rob Tibshirani and Trevor Hastie
Companies: Stanford University and Stanford University and Stanford University
Address: 390 Serra Mall, Stanford, CA, 94305,
Keywords: genomics ; sparsity ; lasso ; canonical correlation ; penalization ; SVD

We present a penalized matrix decomposition, a new framework for computing a low-rank approximation for a matrix. Our low-rank approximation is a generalization of the singular value decomposition. While the singular value decomposition results in singular vectors that have no elements that are exactly equal to zero, our new decomposition results in sparse singular vectors. When this decomposition is applied to a data matrix, it can yield interpretable results. If applied to a variance-covariance matrix, it yields a new method for sparse principal components analysis, and if it is applied to a matrix of cross-products, it results in a new method for sparse canonical correlation analysis (CCA). We show that sparse CCA can be used to perform an integrative analysis of two or more genomic data sets with measurements taken on a single set of samples.

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