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2010

A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization

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A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
The existing Multi-View Learning (MVL) is to discuss how to learn from patterns with multiple information sources and has been proven its superior generalization to the usual Single-View Learning (SVL). However, in most real-world cases there are just single source patterns available such that the existing MVL cannot work. The purpose of this paper is to develop a new multi-view regularization learning for single source patterns. Concretely, for the given single source patterns, we first map them into M feature spaces by M different empirical kernels, then associate each generated feature space with our previous proposed Discriminative Regularization (DR), and finally synthesize M DRs into one single learning process so as to get a new Multi-view Discriminative Regularization (MVDR), where each DR can be taken as one view of the proposed MVDR. The proposed method achieves: 1) the complementarity for multiple views generated from single source patterns; 2) an analytic solution for cl...
Zhe Wang, Songcan Chen, Hui Xue, Zhisong Pan
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where NPL
Authors Zhe Wang, Songcan Chen, Hui Xue, Zhisong Pan
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