Multi-view learning with Universum

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.