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...

• Type: Journal
• Year: 2010
• Where: NPL
• Authors: Zhe Wang, Songcan Chen, Hui Xue, Zhisong Pan
• Keywords: Discriminative Regularization, Multi-view Discriminative Regularization, NPL 2010, Single Source Patterns