Sparse inverse covariance estimation with the graphical lasso

Biostatistics - Tập 9 Số 3 - Trang 432-441 - 2008
Jerome H. Friedman1, Trevor Hastie2, Robert Tibshirani3
1Department of Statistics, Stanford University, CA 94305, USA
2Department of Statistics and Department of Health Research & Policy, Stanford University, CA 94305, USA
3Department of Health Research & Policy and Department of Statistics, Stanford University, CA 94305, USA

Tóm tắt

Abstract We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm—the graphical lasso—that is remarkably fast: It solves a 1000-node problem (∼500000 parameters) in at most a minute and is 30–4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

Từ khóa


Tài liệu tham khảo

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Thông tin
Thông tin xuất bản

Biostatistics

Tập 9 Số 3

432-441

Thông tin tác giả