Robust twin support vector machine for pattern classification

Authors:
Zhiquan Qi;Yingjie Tian;Yong Shi
Affiliations:
Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China
Venue:
Pattern Recognition
Year:
2013

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Abstract

In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via second order cone programming formulations for classification, which can deal with data with measurement noise efficiently. Preliminary experiments confirm the robustness of the proposed method and its superiority to the traditional robust SVM in both computation time and classification accuracy. Remarkably, since there are only inner products about inputs in our dual problems, this makes us apply kernel trick directly for nonlinear cases. Simultaneously we does not need to solve the extra inverse of matrices, which is totally different with existing TWSVMs. In addition, we also show that the TWSVMs are the special case of our robust model and simultaneously give a new dual form of TWSVM by degenerating R-TWSVM, which successfully overcomes the existing shortcomings of TWSVM.