文献类型：期刊文献

英文题名：Multi-view learning with Universum

作者：Wang, Zhe[1];Zhu, Yujin[1];Liu, Wenwen[1];Chen, Zhihua[1];Gao, Daqi[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2014

卷号：70

起止页码：376

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20143600024819);WOS:【SCI-EXPANDED(收录号:WOS:000344209100034)】；

基金：This work was partially supported by Natural Science Foundations of China under Grant Nos. 61272198, 21176077, and 61370174, Innovation Program of Shanghai Municipal Education Commission under Grant No. 14ZZ054, the Fundamental Research Funds for the Central Universities, Shanghai Key Laboratory of Intelligent Information Processing of China under Grant No. IIPL-2012-003, and Provincial Key Laboratory for Computer Information Processing Technology of Soochow University.

语种：英文

外文关键词：Multi-view learning; Universum learning; Regularization learning; Rademacher complexity; Pattern classification

摘要：The traditional Multi-view Learning (MVL) studies how to process patterns with multiple information sources. In practice, the MVL is proven to have a significant advantage over the Single-view Learning (SVL). But in most real-world cases, there are only single-source patterns to be dealt with and the existing MVL is unable to be directly applied. In order to solve this problem, an alternative MVL technique was developed for the single-source patterns through reshaping the original vector representation of the single-source patterns into multiple matrix representations in our previous work. Doing so can effectively bring an improved classification performance. This paper aims to generalize the previous MVL through taking advantage of the Universum examples which do not belong to either class of the classification problem. The newly-proposed generalization can not only inherit the advantage of the previous MVL, but also get a prior domain knowledge of the whole data distribution. To our knowledge, it introduces the Universum technique into the MVL for the first time. In the implementation, our previous MVL named MultiV-MHKS is selected as the learning paradigm and incorporate MultiV-MHKS with the Universum technique, which forms a more flexible MVL with the Universum called UMultiV-MHKS for short. The subsequent experiments validate that the proposed UMultiV-MHKS can effectively improve the classification performance over both the original MultiV-MHKS and some other state-of-the-art algorithms. Finally, it is demonstrated that the UMultiV-MHKS can get a tighter generalization risk bound in terms of the Rademacher complexity. (C) 2014 Elsevier B.V. All rights reserved.