A bayesian framework for learning shared and individual subspaces from multiple data sources
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a me...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Springer
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/32333 |