A multi-class SVM approach based on the l1-norm minimization of the distances between the reduced convex hulls

Miguel Carrasco, Julio López, Sebastián Maldonado

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

All rights reserved. Multi-class classification is an important pattern recognition task that can be addressed accurately and efficiently by Support Vector Machine (SVM). In this work we present a novel SVM-based multi-class classification approach based on the center of the configuration, a point which is equidistant to all classes. The center of the configuration is obtained from the dual formulation by minimizing the distances between the reduced convex hulls using the l1-norm, while the decision functions are subsequently constructed from this point. This work also extends the ideas of Zhou et al. (2002) [37] to multi-class classification. The use of l1-norm provides a single linear programming formulation, which reduces the complexity and confers scalability compared with other multi-class SVM methods based on quadratic programming formulations. Experiments on benchmark datasets demonstrate the virtues of our approach in terms of classification performance and running times compared with various other multi-class SVM methods.
Original languageAmerican English
Pages (from-to)1598-1607
Number of pages10
JournalPattern Recognition
Volume48
Issue number5
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Linear programming
  • Multi-class classification
  • Support vector machines

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