Regularized nonnegative shared subspace learning

Gupta, Sunil Kumar, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2013, Regularized nonnegative shared subspace learning, Data mining and knowledge discovery, vol. 26, no. 1, pp. 57-97, doi: 10.1007/s10618-011-0244-8.

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Title Regularized nonnegative shared subspace learning
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar
Phung, DinhORCID iD for Phung, Dinh
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name Data mining and knowledge discovery
Volume number 26
Issue number 1
Start page 57
End page 97
Total pages 41
Publisher Springer
Place of publication Boston, Mass.
Publication date 2013-01
ISSN 1384-5810
Keyword(s) auxiliary sources
multi-task clustering
nonnegative shared subspace learning
transfer learning
Summary Joint 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.
Language eng
DOI 10.1007/s10618-011-0244-8
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, The Author(s)
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