Feature Selection for SVMs

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.

• Type: Conference
• Year: 2000
• Where: NIPS
• Authors: Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik
• Keywords: Feature Selection, Feature Selection Algorithms, NIPS 2000, NIPS 2007, Support Vector Machines