A bayesian framework for learning shared and individual subspaces from multiple data sources

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...

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Bibliographic Details
Main Authors: Gupta, Sunil, Phung, Dinh, Adams, Brett, Venkatesh, Svetha
Other Authors: J Z Huang
Format: Conference Paper
Published: Springer 2011
Online Access:http://hdl.handle.net/20.500.11937/32333

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