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dc.contributor.authorGupta, Sunil
dc.contributor.authorPhung, Dinh
dc.contributor.authorAdams, Brett
dc.contributor.authorVenkatesh, Svetha
dc.identifier.citationGupta, Sunil Kumar and Phung, Dinh and Adams, Brett and Venkatesh, Svetha. 2011. Regularised nonnegative shared subspace learning. Data Mining Knowledge and Discovery. 23 (1): pp. 57-97.

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.

dc.subjectNonnegative shared subspace learning
dc.subjectAuxiliary sources
dc.subjectTransfer learning
dc.subjectMulti-task clustering
dc.titleRegularised nonnegative shared subspace learning
dc.typeJournal Article
dcterms.source.titleData Mining Knowledge & Discovery
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available

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