期刊
JOURNAL OF COMPUTATIONAL SCIENCE
卷 14, 期 -, 页码 61-68出版社
ELSEVIER
DOI: 10.1016/j.jocs.2016.02.001
关键词
Least squares support vector machine; Sparse; Ramp loss; CCCP; Classification
资金
- National Natural Science Foundation of China [11271361, 61472390, 71331005, 71373023]
- Major International (Regional) Joint Research Project [71110107026]
- New Start Academic Research Project of Beijing Union University [ZK10201409]
- Ministry of Water Resources Special Funds for scientific research on public causes [201301094]
In this paper, we propose a novel sparse least squares support vector machine, named ramp loss least squares support vector machine (RLSSVM), for binary classification. By introducing a non-convex and non-differentiable loss function based on the 8-insensitive loss function, RLSSVM has several improved advantages compared with the plain LSSVM: firstly, it has the sparseness which is controlled by the ramp loss, leading to its better scaling properties; secondly, it can explicitly incorporate noise and outlier suppression in the training process, and thirdly, the non-convexity of RLSSVM can be efficiently solved by the Concave-Convex Procedure (CCCP). Experimental results on several benchmark datasets show the effectiveness of our method. (C) 2016 Elsevier B.V. All rights reserved.
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