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Indefinite twin support vector machine with DC functions programming
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DOI：10.1016/j.patcog.2021.108195.png)
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A B S T R A C T Twin support vector machine (TWSVM) is an efficient algorithm for binary classification. However, the lack of the structural risk minimization principle restrains the generalization of TWSVM and the guaran-tee of convex optimization constraints TWSVM to only use positive semi-definite kernels (PSD). In this paper, we propose a novel TWSVM for indefinite kernel called indefinite twin support vector machine with difference of convex functions programming (ITWSVM-DC). The indefinite T WSVM (IT WSVM) lever-ages a maximum margin regularization term to improve the generalization of TWSVM and a smooth quadratic hinge loss function to make the model continuously differentiable. The representer theorem is applied to the ITWSVM and the convexity of the ITWSVM is analyzed. In order to address the non-convex optimization problem when the kernel is indefinite, a difference of convex functions (DC) is used to decompose the non-convex objective function into the subtraction of two convex functions and a line search method is applied in the DC algorithm to accelerate the convergence rate. A theoretical analysis illustrates that ITWSVM-DC can converge to a local optimum and extensive experiments on indefinite and positive semi-definite kernels show the superiority of ITWSVM-DC. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
SVM
TWSVM
Indefinite kernel
DC Programming
STructural risk minimization principle
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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