The minimum redundancy-maximum relevance approach to building sparse support vector machines

Authors:
Xiaoxing Yang;Ke Tang;Xin Yao
Affiliations:
Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology, University of Science and Technology of China, Hefei, China;Nature Inspired Comp. and Applications Lab., Sch. of Comp. Sci. and Techn., Univ. of Sci. and Techn. of China, Hefei, China and The Center of Excellence for Res. in Comp. Int. and Applications, Sc ...
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

Recently, building sparse SVMs becomes an active research topic due to its potential applications in large scale data mining tasks. One of the most popular approaches to building sparse SVMs is to select a small subset of training samples and employ them as the support vectors. In this paper, we explain that selecting the support vectors is equivalent to selecting a number of columns from the kernel matrix, and is equivalent to selecting a subset of features in the feature selection domain. Hence, we propose to use an effective feature selection algorithm, namely the Minimum Redundancy -- Maximum Relevance (MRMR) algorithm to solve the support vector selection problem. MRMR algorithm was then compared to two existing methods, namely back-fitting (BF) and pre-fitting (PF) algorithms. Preliminary results showed that MRMR generally outperformed BF algorithm while it was inferior to PF algorithm, in terms of generalization performance. However, the MRMR approach was extremely efficient and significantly faster than the two compared algorithms.