PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large margin classifiers based on affine hulls
Hakan Cevikalp, William Triggs, Hasan Serhan Yavuz, Yalcin Kucuk, Mahide Kucuk and Atalay Barkana
Neurocomputing Letters Volume 73, pp. 3160-3168, 2010. ISSN 0925-2312

Abstract

This paper introduces a geometrically inspired large-margin classifier that sometimes outperforms Support Vector Machines (SVMs) in classification problems with limited number of training samples. In contrast to SVMs, we approximate classes with affine hulls of their class samples rather than convex hulls. For any pair of classes approximated with affine hulls, we introduce two solutions to find the best separating hyperplane between them. In the first proposed formulation, we compute the closest points on the affine hulls of classes and connect these two points with a line segment. The optimal separating hyperplane between the two classes is chosen to be the hyperplane that is orthogonal to the line segment and bisects the line. The second formulation is derived by modifying the nu-SVM formulation. Both formulations are extended to the nonlinear case by using the kernel trick. Based on our findings, we also develop a geometric interpretation of the Least Squares SVM classifier and show that it is a special case of the proposed method. Multi-class classification problems are dealt with constructing and combining several binary classifiers as in SVM. The experiments on several databases show that the proposed methods work as good as the SVM classifier if not any better.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7162
Deposited By:William Triggs
Deposited On:07 March 2011