A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2012, A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources, in SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining, Society for Industrial and Applied Mathemations (SIAM), Anaheim, Calif., pp. 200-212.

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Title A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources
Author(s) Gupta, Sunil Kumar
Phung, Dinh
Venkatesh, Svetha
Conference name International Conference on Data Mining (12th : 2012 : Anaheim, Calif.)
Conference location Anaheim, Calif.
Conference dates 26-28 Apr. 2012
Title of proceedings SDM 2012 : Proceedings of the 12th SIAM International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2012
Conference series International Conference on Data Mining
Start page 200
End page 212
Total pages 12
Publisher Society for Industrial and Applied Mathemations (SIAM)
Place of publication Anaheim, Calif.
Summary Joint analysis of multiple data sources is becoming increasingly popular in transfer learning, multi-task learning and cross-domain data mining. One promising approach to model the data jointly is through learning the shared and individual factor subspaces. However, performance of this approach depends on the subspace dimensionalities and the level of sharing needs to be specified a priori. To this end, we propose a nonparametric joint factor analysis framework for modeling multiple related data sources. Our model utilizes the hierarchical beta process as a nonparametric prior to automatically infer the number of shared and individual factors. For posterior inference, we provide a Gibbs sampling scheme using auxiliary variables. The effectiveness of the proposed framework is validated through its application on two real world problems - transfer learning in text and image retrieval.
ISBN 9781611972320
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, by the Society for Industrial and Applied Mathematics
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049274

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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