文献类型：期刊文献

英文题名：A novel multi-view classifier based on Nystr?m approximation

作者：Wang, Zhe[1,2]; Chen, Songcan[2]; Gao, Daqi[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; [2] Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

年份：2011

卷号：38

期号：9

起止页码：11193

外文期刊名：Expert Systems with Applications

收录：EI(收录号：20111913961890)

语种：英文

外文关键词：Classification (of information)

摘要：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 Nystr?m approximation matrices Kps for the gram matrix G of the given single source patterns. Then, we regard the learning on each generated Nystr?m approximation matrix Kp as one view. Finally, different views on Kps 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. ? 2011 Elsevier Ltd. All rights reserved.