Multi-class classification using support vector regression machine with consistency

Wei Jia, Junli Liang, Miaohua Zhang, Xin Ye

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Traditional Support Vector Regression (SVR) Machine acts as approximating a regression function. This paper, however, proposes a novel multi-class classification approach based on the SVR framework, called Support Vector Regression Machine with Consistency (SVRC). The contributions of this paper are: (1) To implement multi-class classification task, were place the margin term with its l1 norm in the SVR framework; (2)To make the training data within the same class possess approximate contributions for the test sample reconstruction and thus improve the robustness, we construct a consistent matrix employing the class information and introduce the penalty term using it; (3) To pay more attention to using fewer possible classes to represent the test sample, and thus improve the accuracy of the test sample reconstruction, we utilize the corresponding local neighborhood relationship of the test sample to design a selection matrix. Experimental results demonstrate that the performance of the proposed method is much better than that of some existing multi-class classification approaches.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479989188
DOIs
StatePublished - 25 Nov 2015
Event5th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015 - Ningbo, Zhejiang, China
Duration: 19 Sep 201522 Sep 2015

Publication series

Name2015 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015

Conference

Conference5th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015
Country/TerritoryChina
CityNingbo, Zhejiang
Period19/09/1522/09/15

Keywords

  • consistent matrix
  • multi-class classification
  • selection matrix
  • sparse representation (SR)
  • Support vector regression machine with consistency (SVRC)

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