Large Margin Feature Selection for Support Vector Machine

Wei Pan; Pei Jun Ma; Xiao Hong Su

doi:10.4028/www.scientific.net/AMM.274.161

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 274Large Margin Feature Selection for Support Vector...

Large Margin Feature Selection for Support Vector Machine

Abstract:

Feature selection is an preprocessing step in pattern analysis and machine learning. In this paper, we design a algorithm for feature subset. We present L1-norm regularization technique for sparse feature weight. Margin loss are introduced to evaluate features, and we employs gradient descent to search the optimal solution to maximize margin. The proposed technique is tested on UCI data sets. Compared with four margin based loss functions for SVM, the proposed technique is effective and efficient.

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Periodical:

Applied Mechanics and Materials (Volume 274)

Pages:

161-164

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.274.161

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Online since:

January 2013

Authors:

Wei Pan, Pei Jun Ma, Xiao Hong Su

Keywords:

Feature Selection, Margin, Support Vector Machine (SVM)

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