Multi-view L2-SVM and its multi-view core vector machine

Chengquan Huang, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets.
Original languageEnglish
Pages (from-to)110-125
Number of pages16
JournalNeural Networks
Volume75
DOIs
Publication statusPublished - 1 Mar 2016

Keywords

  • Core vector machine
  • L2-SVM
  • Large scale multi-view datasets
  • Multi-view learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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