Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Yves Grandvalet, Stéphane Canu
This paper introduces an algorithm for the automatic relevance determi- nation of input variables in kernelized Support Vector Machines. Rele- vance is measured by scale factors deﬁning the input space metric, and feature selection is performed by assigning zero weights to irrelevant variables. The metric is automatically tuned by the minimization of the standard SVM empirical risk, where scale factors are added to the usual set of parameters deﬁning the classiﬁer. Feature selection is achieved by constraints encouraging the sparsity of scale factors. The resulting algorithm compares favorably to state-of-the-art feature selection proce- dures and demonstrates its effectiveness on a demanding facial expres- sion recognition problem.