Multi-View Classification Method Based on Cross-View Constraints
XUE Hui1,2, CHEN Song-Can3, LIU Jie1, HUANG Ji-Jian1
1.Key Laboratory of Computer Network and Information Integration, Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 210096 2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093 3.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016
Abstract A multi-view paired model, cross-view constraint, is taken into account and thus the pairwise constraints are extended in single-view learning. Instead of the strict paired constraints, the weaker constraint information is used, i.e. whether the data pairs between different views belong to the same class or not. Therefore, the cross-view constraints can not only include the totally paired constraints, but also be generalized to the case that the data are unpaired completely. Based on the cross-view constraints, a multi-view classification method is proposed. The proposed method can deeply mine the potential discriminative information in cross-view constraints and utilize the structural information of the data pairs as well. Experimental results demonstrate the effectiveness of the proposed method.
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2014, Vol. 27 
