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Centre for Pattern Recognition and Data Analytics
School of Information Technology
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GEELONG VIC 3220
S.K. Gupta, D. Phung, and S. Venkatesh. A Bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources. In Proceedings of 12th SIAM International Conference on Data Mining, pages 200-211, 2012.
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.
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27th February 2015