Optimal Feature Selection for Support Vector Machines

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Abstract

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.

Citation

Paper thumbnail Optimal Feature Selection for Support Vector Machines. Nguyen, M.H., De la Torre, F. (2010) Pattern Recognition , 43(3), 584 - 591. [PDF] [Bibtex]
Paper thumbnail Facial Feature Detection with Optimal Pixel Reduction SVMs. Nguyen, M.H., Perez, J. and De la Torre, F. (2008) Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition. [PDF] [Bibtex]

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Acknowledgements and Funding

This work was supported by the US Naval Research Laboratory under Contract no. N00173-07-C-2040. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US Naval Research Laboratory.

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