A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
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The Journal of Machine Learning Research
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Iterative RELIEF for feature weighting
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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
Feature Selection for Nonlinear Kernel Support Vector Machines
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Subset selection for efficient SVM tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Image retrieval employing genetic dissimilarity weighting and feature space transformation functions
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Computers and Operations Research
Feature space optimization for content-based image retrieval
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Feature words that classify problem sentence in scientific article
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Large Margin Subspace Learning for feature selection
Pattern Recognition
Applied Soft Computing
Learning discriminative localization from weakly labeled data
Pattern Recognition
Modeling observer stress for typical real environments
Expert Systems with Applications: An International Journal
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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.