In:
Journal of Applied Mathematics, Hindawi Limited, Vol. 2014 ( 2014), p. 1-16
Abstract:
This paper studies feature selection for support vector machine (SVM). By the use of the L 1 / 2 regularization technique, we propose a new model L 1 / 2 -SVM. To solve this nonconvex and non-Lipschitz optimization problem, we first transform it into an equivalent quadratic constrained optimization model with linear objective function and then develop an interior point algorithm. We establish the convergence of the proposed algorithm. Our experiments with artificial data and real data demonstrate that the L 1 / 2 -SVM model works well and the proposed algorithm is more effective than some popular methods in selecting relevant features and improving classification performance.
Type of Medium:
Online Resource
ISSN:
1110-757X
,
1687-0042
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2014
detail.hit.zdb_id:
2578385-3
SSG:
17,1
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