期刊
BIOMETRIKA
卷 100, 期 4, 页码 901-920出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ast036
关键词
Low-rank approximation; Nuclear norm penalization; Reduced rank regression; Singular value decomposition
资金
- U.S. National Institutes of Health
- National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1021292] Funding Source: National Science Foundation
We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally nonconvex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed nonconvex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy.
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