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

Content Provider	Springer Nature Link
Author	Wang, Zhe Chen, Songcan Xue, Hui Pan, Zhisong
Copyright Year	2010
Abstract	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 classification; (3) a direct optimization formulation for multi-class problems without one-against-all or one-against-one strategies.
Starting Page	159
Ending Page	175
Page Count	17
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	31
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2010-03-11
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Discriminative Regularization Multi-View Learning Single source patterns Multi-class problem Classification Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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