Bayesian multiview dimensionality reduction for learning predictive subspaces

Mehmet Gönen, Gülefşan Bozkurt Gönen, Fikret Gürgen

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

3 Scopus citations

Abstract

Multiview learning basically tries to exploit different feature representations to obtain better learners. For example, in video and image recognition problems, there are many possible feature representations such as color- and texture-based features. There are two common ways of exploiting multiple views: forcing similarity (i) in predictions and (ii) in latent subspace. In this paper, we introduce a novel Bayesian multiview dimensionality reduction method coupled with supervised learning to find predictive subspaces and its inference details. Experiments show that our proposed method obtains very good results on image recognition tasks in terms of classification and retrieval performances.

Original languageEnglish (US)
Title of host publicationECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
PublisherIOS Press
Pages387-392
Number of pages6
ISBN (Electronic)9781614994183
DOIs
StatePublished - Jan 1 2014
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: Aug 18 2014Aug 22 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume263
ISSN (Print)0922-6389

Other

Other21st European Conference on Artificial Intelligence, ECAI 2014
CountryCzech Republic
CityPrague
Period8/18/148/22/14

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Gönen, M., Gönen, G. B., & Gürgen, F. (2014). Bayesian multiview dimensionality reduction for learning predictive subspaces. In T. Schaub, G. Friedrich, & B. O'Sullivan (Eds.), ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings (pp. 387-392). (Frontiers in Artificial Intelligence and Applications; Vol. 263). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-387