Global Convergence Analysis of Decomposition Methods for Support Vector Regression

Authors:
Jun Guo;Norikazu Takahashi
Affiliations:
Japan Society for the Promotion of Science School of Information Science and Technology, East China Normal Univ., Shanghai, China 200241 and Department of Computer Science and Communication Engine ...;Department of Computer Science and Communication Engineering, Kyushu University, Nishi-ku, Japan 819-0395
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Decomposition method has been widely used to efficiently solve the large size quadratic programming (QP) problems arising in support vector regression (SVR). In a decomposition method, a large QP problem is decomposed into a series of smaller QP subproblems, which can be solved much faster than the original one. In this paper, we analyze the global convergence of decomposition methods for SVR. We will show the decomposition methods for the convex programming problem formulated by Flake and Lawrence always stop within a finite number of iterations.