A novel multi-view classifier based on Nystrom approximation

作者：Wang, Zhe; Chen, Songcan^*; Gao, Daqi

来源：Expert Systems with Applications, 2011, 38(9): 11193-11200.

DOI：10.1016/j.eswa.2011.02.166

• Multi-view learning
• Single-view patterns
• Kernel-based method
• Nystrom approximations
• Rademacher complexity
• Classifier design

The existing multi-view learning (MVL) is learning from patterns with multiple information sources and has been proven its superior generalization to the conventional single-view learning (SVL). However, in most real-world cases, researchers just have single source patterns available in which the existing MVL is uneasily directly applied. The purpose of this paper is to solve this problem and develop a novel kernel-based MVL technique for single source patterns. In practice, we first generate different Nystrom approximation matrices K(p)s for the gram matrix G of the given single source patterns. Then, we regard the learning on each generated Nystram approximation matrix K-p as one view. Finally, different views on K(p)s are synthesized into a novel multi-view classifier. In doing so, the proposed algorithm as a MVL machine can directly work on single source patterns and simultaneously achieve: (1) low-cost learning; (2) effectiveness; (3) the same Rademacher complexity as the single-view KMHKS; (4) ease of extension to any other kernel-based learning algorithms.

• 出版日期2011-9
• 单位南京航空航天大学; 华东理工大学