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

Sunil Kumar Gupta, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
Number of pages12
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventSIAM International Conference on Data Mining 2012 - Anaheim, United States of America
Duration: 26 Apr 201228 Apr 2012
Conference number: 12th

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012


ConferenceSIAM International Conference on Data Mining 2012
Abbreviated titleSDM 2012
Country/TerritoryUnited States of America

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