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A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources
conference contribution
posted on 2012-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshJoint 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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Event
International Conference on Data Mining (12th : 2012 : Anaheim, Calif.)Pagination
200 - 212Publisher
Society for Industrial and Applied Mathemations (SIAM)Location
Anaheim, Calif.Place of publication
Anaheim, Calif.Start date
2012-04-26End date
2012-04-28ISBN-13
9781611972320Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, by the Society for Industrial and Applied MathematicsTitle of proceedings
SDM 2012 : Proceedings of the 12th SIAM International Conference on Data MiningUsage metrics
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