A slice sampler for restricted hierarchical beta process with applications to shared subspace learning

Sunil Kumar Gupta, Dinh Phung, Svetha Venkatesh

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

8 Citations (Scopus)

Abstract

Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different sources. We derive a slice sampler for this model, enabling tractable inference even when the likelihood and the prior over parameters are non-conjugate. This allows the application of the model in much wider contexts without restrictions. We present two different data generative models - a linear Gaussian- Gaussian model for real valued data and a linear Poisson-gamma model for count data. Encouraging transfer learning results are shown for two real world applications - text modeling and content based image retrieval.

Original languageEnglish
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
Pages316-325
Number of pages10
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventConference in Uncertainty in Artificial Intelligence 2012 - Catalina Island, United States of America
Duration: 15 Aug 201217 Aug 2012
Conference number: 28th
https://dl.acm.org/doi/proceedings/10.5555/3020652 (Proceedings)

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Conference

ConferenceConference in Uncertainty in Artificial Intelligence 2012
Abbreviated titleUAI 2012
CountryUnited States of America
CityCatalina Island
Period15/08/1217/08/12
Internet address

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