Multi-view Discriminant Transfer learning

Pei Yang, Wei Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

We study to incorporate multiple views of data in a perceptive transfer learning framework and propose a Multi-view Discriminant Transfer (MDT) learning approach for domain adaptation. The main idea is to find the optimal discriminant weight vectors for each view such that the correlation between the two-view projected data is maximized, while both the domain discrepancy and the view disagreement are minimized simultaneously. Furthermore, we analyze MDT theoretically from discriminant analysis perspective to explain the condition and reason, under which the proposed method is not applicable. The analytical results allow us to investigate whether there exist within-view and/or betweenview conflicts, and thus provides a deep insight into whether the transfer learning algorithm work properly or not in the view-based problems and the combined learning problem. Experiments show that MDT significantly outperforms the state-of-the-art baselines including some typical multi-view learning approaches in single- or cross-domain.

Original languageEnglish
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages1848-1854
Number of pages7
Publication statusPublished - 1 Dec 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: 3 Aug 20139 Aug 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period3/8/139/8/13

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Yang, P., & Gao, W. (2013). Multi-view Discriminant Transfer learning. In IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence (pp. 1848-1854). (IJCAI International Joint Conference on Artificial Intelligence).