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An efficient multi-label support vector machine with a zero label
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DOI：10.1016/j.eswa.2011.09.138.png)
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Abstract
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Existing multi-label support vector machine (Rank-SVM) has an extremely high computational complexity and lacks an intrinsic zero point to determine relevant labels. In this paper, we propose a novel support vector machine for multi-label classification through both simplifying Rank-SVM and adding a zero label, resulting into a quadratic programming problem in which each class has an independent equality constraint. When Frank-Wolfe method is used to solve our quadratic programming problem iteratively, our entire linear programming problem of each step is divided into a series of sub-problems, which dramatically reduces computational cost. It is illustrated that for famous Yeast data set our training procedure runs about 12 times faster than Rank-SVM does under C++ environment. Experiments from five benchmark data sets show that our method is a powerful candidate for multi-label classification, compared with five state-of-the-art multi-label classification techniques. (C) 2011 Elsevier Ltd. All rights reserved.
Keywords:
Support vector machine
Multi-label classification
Frank-Wolfe method
Linear programming
Quadratic programming
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