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dc.contributor.authorPeng, Chengbin
dc.contributor.authorWong, Kachun
dc.contributor.authorRockwood, Alyn
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorJiang, Jinling
dc.contributor.authorKeyes, David E.
dc.date.accessioned2015-08-04T07:05:33Z
dc.date.available2015-08-04T07:05:33Z
dc.date.issued2012-12
dc.identifier.isbn9780769549057
dc.identifier.issn15504786
dc.identifier.doi10.1109/ICDM.2012.106
dc.identifier.urihttp://hdl.handle.net/10754/564631
dc.description.abstractNon-negative matrix factorization (NMF) provides the advantage of parts-based data representation through additive only combinations. It has been widely adopted in areas like item recommending, text mining, data clustering, speech denoising, etc. In this paper, we provide an algorithm that allows the factorization to have linear or approximatly linear constraints with respect to each factor. We prove that if the constraint function is linear, algorithms within our multiplicative framework will converge. This theory supports a large variety of equality and inequality constraints, and can facilitate application of NMF to a much larger domain. Taking the recommender system as an example, we demonstrate how a specialized weighted and constrained NMF algorithm can be developed to fit exactly for the problem, and the tests justify that our constraints improve the performance for both weighted and unweighted NMF algorithms under several different metrics. In particular, on the Movielens data with 94% of items, the Constrained NMF improves recall rate 3% compared to SVD50 and 45% compared to SVD150, which were reported as the best two in the top-N metric. © 2012 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectLinear constraints
dc.subjectMultiplicative algorithm
dc.subjectNon-negative matrix factorization
dc.titleMultiplicative algorithms for constrained non-negative matrix factorization
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journal2012 IEEE 12th International Conference on Data Mining
dc.conference.date10 December 2012 through 13 December 2012
dc.conference.name12th IEEE International Conference on Data Mining, ICDM 2012
dc.conference.locationBrussels
dc.contributor.institutionDepartment of Computer Science, University of Toronto, Toronto, Canada
dc.contributor.institutionDepartment of Computer Science, Aalborg University, Aalborg, Denmark
kaust.personPeng, Chengbin
kaust.personZhang, Xiangliang
kaust.personKeyes, David E.
kaust.personRockwood, Alyn


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