Multiclass support vector classification via coding and regression

Authors:
Pei-Chun Chen;Kuang-Yao Lee;Tsung-Ju Lee;Yuh-Jye Lee;Su-Yun Huang
Affiliations:
Bioinformatics and Biostatistics Core, Research Center for Medical Excellence, National Taiwan University, 7F., No. 2, Syu-jhou Road, Taipei 10055, Taiwan, ROC;Department of Statistics, Pennsylvania State University, USA;Computer Science and Engineering, National Chiao-Tung University, Taiwan;Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan;Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Venue:
Neurocomputing
Year:
2010

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Abstract

The multiclass classification problem is considered and resolved through coding and regression. There are various coding schemes for transforming class labels into response scores. An equivalence notion of coding schemes is developed, and the regression approach is adopted for extracting a low-dimensional discriminant feature subspace. This feature subspace can be a linear subspace of the column span of original input data or kernel-mapped feature data. The classification training and prediction are carried out in this feature subspace using a linear classifier, which lead to a simple and computationally light but yet powerful toolkit for classification. Experimental results, including prediction ability and CPU time comparison with LIBSVM, show that the regression-based approach is a competent alternative for the multiclass problem.