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A matrix factorization framework for jointly analyzing multiple nonnegative data sources

Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2014, A matrix factorization framework for jointly analyzing multiple nonnegative data sources. In Yada, Katsutoshi (ed), Data mining for service, Springer, Berlin, Germany, pp.151-170, doi: 10.1007/978-3-642-45252-9.

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Title A matrix factorization framework for jointly analyzing multiple nonnegative data sources
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar
Phung, DinhORCID iD for Phung, Dinh
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Title of book Data mining for service
Editor(s) Yada, Katsutoshi
Publication date 2014
Series Studies in big data; v.3
Chapter number 10
Total chapters 15
Start page 151
End page 170
Total pages 20
Publisher Springer
Place of Publication Berlin, Germany
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 chapter, 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.
ISBN 9783642452529
Language eng
DOI 10.1007/978-3-642-45252-9
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1.1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, Springer
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Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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