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Regularized nonnegative shared subspace learning
journal contribution
posted on 2013-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, B Adams, Svetha VenkateshSvetha VenkateshJoint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.
History
Journal
Data mining and knowledge discoveryVolume
26Issue
1Pagination
57 - 97Publisher
SpringerLocation
Boston, Mass.Publisher DOI
ISSN
1384-5810eISSN
1573-756XLanguage
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2011, The Author(s)Usage metrics
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Keywords
auxiliary sourcesmulti-task clusteringnonnegative shared subspace learningtransfer learningScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer ScienceMATRIX FACTORIZATIONALGORITHMSFRAMEWORKInformation SystemsArtificial Intelligence and Image ProcessingData Format
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