Article ID Journal Published Year Pages File Type
430041 Journal of Computational Science 2016 8 Pages PDF
Abstract

•A novel support vector machine (RLSSVM) for binary classification.•RLSSVM has the sparseness which is controlled by the ramp loss.•The non-convexity of RLSSVM can be efficiently solved by the Concave-Convex Procedure (CCCP).

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 ɛ-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.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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