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Optimal feature selection for support vector machines

11 years 10 months ago
Optimal feature selection for support vector machines
Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance. Key words: Support Vector Machine, Feature selection, Feature extraction
Minh Hoai Nguyen, Fernando De la Torre
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PR
Authors Minh Hoai Nguyen, Fernando De la Torre
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