Sparse high-dimensional fractional-norm support vector machine via DC programming

This paper considers a class of feature selecting support vector machines (SVMs) based on Lq-norm regularization, where q∈(0,1). The standard SVM [Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, NY.] minimizes the hinge loss function subject to the L2-norm penalty. Recently, L...

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Veröffentlicht in:	Computational statistics & data analysis Jg. 67; S. 136 - 148
Hauptverfasser:	Guan, Wei, Gray, Alexander
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2013
Schlagworte:	Algorithms Approximation data collection Difference of Convex functions programming Direct current Feature selection Fractional-norm SVM Low sample size high dimensional data set Mathematical analysis Mathematical models Optimization Programming Reweighted [formula omitted]-norm SVM Support vector machine Support vector machines Feature selection Fractional-norm SVM Reweighted L1-norm SVM Support vector machine Difference of Convex functions programming Low sample size high dimensional data set
ISSN:	0167-9473, 1872-7352
Online-Zugang:	Volltext
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