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

• Type: Journal
• Year: 2010
• Where: PR
• Authors: Minh Hoai Nguyen, Fernando De la Torre
• Keywords: Feature Selection, PR 2010, Svm Parameter, Vector Machine