Decomposition methods for linear support vector machines

Authors:
Wei-Chun Kao;Kai-Min Chung;Chia-Liang Sun;Chih-Jen Lin
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
Venue:
Neural Computation
Year:
2004

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Abstract

In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.