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Knowledge transfer across different domain data with multiple views

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Abstract

In many real-world applications in the areas of data mining, the distributions of testing data are different from that of training data. And on the other hand, many data are often represented by multiple views which are of importance to learning. However, little work has been done for it. In this paper, we explored to leverage the multi-view information across different domains for knowledge transfer. We proposed a novel transfer learning model which integrates the domain distance and view consistency into a 2-view support vector machine framework, namely DV2S. The objective of DV2S is to find the optimal feature mapping such that under the projections the classification margin is maximized, while both the domain distance and the disagreement between multiple views are minimized simultaneously. Experiments showed that DV2S outperforms a variety of state-of-the-art algorithms.

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Authors and Affiliations

  1. Department of Computer Science, South China University of Technology, Guangzhou, China

    Qi Tan, Huifang Deng & Pei Yang

  2. Department of Computer Science, South China Normal University, Guangzhou, China

    Qi Tan

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  1. Qi Tan
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  2. Huifang Deng
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  3. Pei Yang
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Correspondence to Pei Yang.

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Tan, Q., Deng, H. & Yang, P. Knowledge transfer across different domain data with multiple views. Neural Comput & Applic 25, 15–23 (2014). https://doi.org/10.1007/s00521-013-1432-9

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