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

Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2011, A Bayesian framework for learning shared and individual subspaces from multiple data sources, in PAKDD 2011 : Advances in knowledge discovery and data mining : 15th Pacific-Asia Conference, Shenzhen, China, May 24-27, 2011, proceedings, part II, Springer-Verlag, Berlin, Germany, pp. 136-147, doi: 10.1007/978-3-642-20841-6_12.

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Title A Bayesian framework for learning shared and individual subspaces from multiple data sources
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Knowledge Discovery and Data Mining. Pacific-Asia Conference (15th : 2011 : Shenzhen, China)
Conference location Shenzhen, China
Conference dates 24-27 May. 2011
Title of proceedings PAKDD 2011 : Advances in knowledge discovery and data mining : 15th Pacific-Asia Conference, Shenzhen, China, May 24-27, 2011, proceedings, part II
Editor(s) Huang, Joshua Zhexue
Cao, Longbing
Srivastava, Jaideep
Publication date 2011
Series Lecture notes in artificial intelligence : 6635
Conference series Pacific-Asia Conference on Knowledge Discovery and Data Mining
Start page 136
End page 147
Total pages 12
Publisher Springer-Verlag
Place of publication Berlin, Germany
Keyword(s) Bayesian formulation
Bayesian frameworks
data sets
data source
efficient algorithm
formal framework
Gibbs sampling
heterogeneous data sources
matrix factorizations
multiple data sources
mutual knowledge
partial knowledge
social media
Summary 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 method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.
ISBN 3642208460
9783642208461
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-642-20841-6_12
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044674

Document type: Conference Paper
Collections: School of Information Technology
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