A Novel Multi-view Classifier Based on Matrixization

• 1、Department of Computer Science & Engineering, East China University of Science & Technology
• 2、Department of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics

Abstract：This paper aims at developing a new MVL technique for single source patterns. First, we reshape the original vector representation of single source patterns into multiple matrix representations. Doing so can change the original architecture of a given base classifier into different sub-ones. Each newly-generated sub-classifier can classify the patterns represented with the matrix, and is taken as one view of the original base classifier. As a result, a set of sub-classifiers with different views are come into being. Then, a joint rather than separated learning process for the multi-view sub-classifiers is developed. In practice, the original base classifier employs the vector-pattern-oriented Ho-Kashyap classifier with regularization learning (called MHKS) as a paradigm that is not limited to MHKS. Finally, the feasibility and effectiveness of the proposed multi-view-combined classifier, named MultiV-MHKS, is demonstrated by the experimental results on benchmark data sets.

Keywords： Pattern Representation Matrix Pattern Vector Pattern Classifier design Multi-view learning