A new scatter-based multi-class support vector machine

Robert Jenssen, Marius Kloft, Sören Sonnenburg, Alexander Zien, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. We identify the associated primal problem and develop a fast chunking-based optimizer. Promising results are reported, also compared to the state-of-the-art, at lower computational complexity.

Original languageEnglish
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
Publication statusPublished - 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: 2011 Sep 182011 Sep 21

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period11/9/1811/9/21

Keywords

  • multi-class
  • scatter
  • μ-SVM

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Jenssen, R., Kloft, M., Sonnenburg, S., Zien, A., & Müller, K. R. (2011). A new scatter-based multi-class support vector machine. In 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011 [6064625] (IEEE International Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2011.6064625