期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 22, 期 2, 页码 396-415出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2012.680324
关键词
Coordinate descent; Elastic net; Hubernet; Large margin classifiers; Majorization-minimization; SVM
资金
- NSF [DMS-08-46068]
The hybrid Huberized support vector machine (HHSVM) has proved its advantages over the l(1) support vector machine (SVM) in terms of classification and variable selection. Similar to the l(1) SVM, the HHSVM enjoys a piecewise linear path property and can be computed by a least-angle regression (LARS)-type piecewise linear solution path algorithm. In this article, we propose a generalized coordinate descent (GCD) algorithm for computing the solution path of the HHSVM. The GCD algorithm takes advantage of a majorization-minimization trick to make each coordinatewise update simple and efficient. Extensive numerical experiments show that the GCD algorithm is much faster than the LARS-type path algorithm. We further extend the GCD algorithm to solve a class of elastic net penalized large margin classifiers, demonstrating the generality of the GCD algorithm. We have implemented the GCD algorithm in a publicly available R package gcdnet.
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