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 language | English |
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Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012 |
Pages | 316-325 |
Number of pages | 10 |
Publication status | Published - 1 Dec 2012 |
Externally published | Yes |
Event | Conference in Uncertainty in Artificial Intelligence 2012 - Catalina Island, United States of America Duration: 15 Aug 2012 → 17 Aug 2012 Conference number: 28th https://dl.acm.org/doi/proceedings/10.5555/3020652 (Proceedings) |
Publication series
Name | Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012 |
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Conference
Conference | Conference in Uncertainty in Artificial Intelligence 2012 |
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Abbreviated title | UAI 2012 |
Country/Territory | United States of America |
City | Catalina Island |
Period | 15/08/12 → 17/08/12 |
Internet address |
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