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A matrix factorization framework for jointly analyzing multiple nonnegative data sources
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posted on 2014-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, B Adams, Svetha VenkateshSvetha VenkateshNonnegative 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.
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Title of book
Data mining for serviceVolume
3Series
Studies in big data; v.3Chapter number
10Pagination
151 - 170Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISBN-13
9783642452529Language
engPublication classification
B Book chapter; B1.1 Book chapterCopyright notice
2014, SpringerExtent
15Editor/Contributor(s)
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