DCA based algorithms for feature selection in semi-supervised support vector machines

Authors:
Hoai Minh Le;Hoai An Le Thi;Manh Cuong Nguyen
Affiliations:
Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, University of Lorraine, Metz, France;Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, University of Lorraine, Metz, France;Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, University of Lorraine, Metz, France
Venue:
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2013

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Abstract

In this paper, we develop an efficient method for feature selection in Semi-Supervised Support Vector Machine (S3VM). Using an appropriate continuous approximation of the l0−norm, we reformulate the feature selection S3VM problem as a DC (Difference of Convex functions) program. DCA (DC Algorithm), an innovative approach in nonconvex programming is then developed to solve the resulting problem. Computational experiments on several real-world datasets show the efficiency and the scalability of our method.