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

Version 2 2024-06-03, 17:12
Version 1 2014-10-28, 09:38
conference contribution
posted on 2024-06-03, 17:12 authored by Sunil GuptaSunil Gupta, D Phung, B Adams, Svetha VenkateshSvetha Venkatesh
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.

History

Pagination

136-147

Location

Shenzhen, China

Start date

2011-05-24

End date

2011-05-27

ISSN

0302-9743

ISBN-13

9783642208461

ISBN-10

3642208460

Language

eng

Publication classification

E1.1 Full written paper - refereed, E Conference publication

Copyright notice

2011, Springer-Verlag Berlin Heidelberg

Extent

45

Editor/Contributor(s)

Huang J, Cao L, Srivastava J

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

Event

Knowledge Discovery and Data Mining. Pacific-Asia Conference (15th : 2011 : Shenzhen, China)

Publisher

Springer-Verlag

Place of publication

Berlin, Germany

Series

Lecture notes in artificial intelligence : 6635