DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines

Hoai Minh Le,Hoai An Le Thi,Manh Cuong Nguyen

doi:10.1007/978-3-642-39712-7_41

DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines

Hoai Minh Le, Hoai An Le Thi + Show 1 more

https://doi.org/10.1007/978-3-642-39712-7_41

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Publication Date: Jan 1, 2013

Citations: 15

Affiliation: Laboratory of Microstructure Studies and Mechanics of Materials, Université de Lorraine

#Semi-supervised Support Vector Machines #Semi-Supervised Support Vector Machine + Show 8 more

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Abstract

AbstractIn this paper, we develop an efficient method for feature selection in Semi-Supervised Support Vector Machine (S3VM). Using an appropriate continuous approximation of the l 0 − 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.KeywordsFeature SelectionS3VMDC ProgrammingDCA

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