Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43923
Title: Multi-view L2-SVM and its multi-view core vector machine
Authors: Huang, C
Chung, FL 
Wang, S
Keywords: Core vector machine
L2-SVM
Large scale multi-view datasets
Multi-view learning
Issue Date: 2016
Publisher: Pergamon Press
Source: Neural networks, 2016, v. 75, p. 110-125 How to cite?
Journal: Neural networks 
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.
URI: http://hdl.handle.net/10397/43923
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2015.12.004
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