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A matrix factorization framework for jointly analyzing multiple nonnegative data source

Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2011, A matrix factorization framework for jointly analyzing multiple nonnegative data source, in Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, [Mesa, Ariz.], pp. 6-15.

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Title A matrix factorization framework for jointly analyzing multiple nonnegative data source
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 Workshop on Text Mining (9th : 2011 : Mesa, Ariz.)
Conference location Mesa, Ariz.
Conference dates 30 Apr. 2011
Title of proceedings Proceedings of the 9th Workshop on Text Mining, in conjunction with the 11th SIAM International Conference on Data Mining
Editor(s) [Unknown]
Publication date 2011
Conference series Workshop on Text Mining
Start page 6
End page 15
Total pages 10
Publisher Society for Industrial and Applied Mathematics
Place of publication [Mesa, Ariz.]
Keyword(s) text mining
nonnegative matrix factorization
arbitrary sharing configurations
data mining
data sources
Summary Nonnegative matrix factorization based methods provide one of the simplest and most effective approaches to text mining. However, their applicability is mainly limited to analyzing a single data source. In this paper, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing configurations encountered in real world data. We derive an efficient algorithm for learning the factorization and show that its convergence is theoretically guaranteed. We demonstrate the utility and effectiveness of the proposed framework in two real-world applications–improving social media retrieval using auxiliary sources and cross-social media retrieval. Representing each social media source using their textual tags, for both applications, we show that retrieval performance exceeds the existing state-of-the-art techniques. The proposed solution provides a generic framework and can be applicable to a wider context in data mining wherever one needs to exploit mutual and individual knowledge present across multiple data sources.
Language eng
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044853

Document type: Conference Paper
Collection: School of Information Technology
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