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Machine Learning

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Nonlinear Feature Selection with the Potential Support Vector Machine
Citation key Hochreiter2006c
Author Hochreiter, S. and Obermayer, K.
Title of Book Feature Extraction: Foundations and Applications
Pages 419 – 438
Year 2006
ISBN 978-3-540-35487-1, 978-3-540-35488-8
ISSN 1434-9922
DOI 10.1007/978-3-540-35488-8_20
Editor Guyon, I. and Gunn, S. and Nikravesh, M. and Zadeh, L.
Publisher Springer Berlin Heidelberg
Abstract We describe the “Potential Support Vector Machine” (P-SVM) which is a new filter method for feature selection. The idea of the P-SVM feature selection is to exchange the role of features and data points in order to construct “support features”. The “support features” are the selected features. The P-SVM uses a novel objective function and novel constraints — one constraint for each feature. As with standard SVMs, the objective function represents a complexity or capacity measure whereas the constraints enforce low empirical error. In this contribution we extend the P-SVM in two directions. First, we introduce a parameter which controls the redundancy among the selected features. Secondly, we propose a nonlinear version of the P-SVM feature selection which is based on neural network techniques. Finally, the linear and nonlinear P-SVM feature selection approach is demonstrated on toy data sets and on data sets from the NIPS 2003 feature selection challenge.
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