On L_1-Norm Multi-class Support Vector Machines

Authors:
Lifeng Wang;Xiaotong Shen;Yuan F. Zheng
Affiliations:
The University of Minnesota, USA;The University of Minnesota, USA;The Ohio State University, USA
Venue:
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Year:
2006

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Abstract

Binary Support Vector Machines (SVM) have proven effec- tive in classiffication. However, problems remain with respect to feature selection in multi-class classification. This article proposes a novel multi-class SVM, which performs classifica- tion and feature selection simultaneously via L_1-norm penal- ized sparse representations. The proposed methodology, to- gether with our developed regularization solution path, per- mits feature selection within the framework of classiffication. The operational characteristics of the proposed methodol- ogy is examined via both simulated and benchmark exam- ples, and is compared to some competitors in terms of the accuracy of prediction and feature selection. The numeri- cal results suggest that the proposed methodology is highly competitive.