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2012 IEEE 12th International Conference on Data Mining (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 13, 2012
ISSN: 1550-4786
ISBN: 978-1-4673-4649-8
pp: 864-869
Multiple feature views arise in various important data classification scenarios. However, finding a consensus feature view from multiple feature views for a classifier is still a challenging task. We present a new classification framework using the multi-label correlation information to address the problem of simultaneously combining multiple feature views and maximum margin classification. Under this framework, we propose a novel algorithm that iteratively computes the multiple view feature mapping matrices, the consensus feature view representation, and the coefficients of the classifier. Extensive experimental evaluations demonstrate the effectiveness and promise of this framework as well as the algorithm for discovering a consensus view from multiple feature views.
consensus representation, feature mapping, multi-view learning, label dependence maximization, maximum margin classification

Z. Fang and Z. (. Zhang, "Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification," 2012 IEEE 12th International Conference on Data Mining(ICDM), Brussels, Belgium Belgium, 2012, pp. 864-869.
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