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Conference Paper: Structured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization

TitleStructured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization
Authors
Pan, JunjunNg, Michael K.Zhang, Xiongjun
Issue Date2018
Citation
Proceedings - International Conference on Pattern Recognition, 2018, v. 2018-August, p. 459-464 How to Cite?
DOI: http://dx.doi.org/10.1109/ICPR.2018.8545862
Abstract© 2018 IEEE. Orthogonal nonnegative matrix factorization plays an important role for data clustering and machine learning. In this paper, we propose a new optimization model for orthogonal nonnegative matrix factorization based on the structural properties of orthogonal nonnegative matrix. The new model can be solved by a novel convex relaxation technique which can be employed quite efficiently. Numerical examples in document clustering, image segmentation and hyperspectral unmixing are used to test the performance of the proposed model. The performance of our method is better than the other testing methods in terms of clustering accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/276627
ISSN
1051-4651
2023 SCImago Journal Rankings: 0.584

 

DC FieldValueLanguage
dc.contributor.authorPan, Junjun-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorZhang, Xiongjun-
dc.date.accessioned2019-09-18T08:34:10Z-
dc.date.available2019-09-18T08:34:10Z-
dc.date.issued2018-
dc.identifier.citationProceedings - International Conference on Pattern Recognition, 2018, v. 2018-August, p. 459-464-
dc.identifier.issn1051-4651-
dc.identifier.urihttp://hdl.handle.net/10722/276627-
dc.description.abstract© 2018 IEEE. Orthogonal nonnegative matrix factorization plays an important role for data clustering and machine learning. In this paper, we propose a new optimization model for orthogonal nonnegative matrix factorization based on the structural properties of orthogonal nonnegative matrix. The new model can be solved by a novel convex relaxation technique which can be employed quite efficiently. Numerical examples in document clustering, image segmentation and hyperspectral unmixing are used to test the performance of the proposed model. The performance of our method is better than the other testing methods in terms of clustering accuracy.-
dc.languageeng-
dc.relation.ispartofProceedings - International Conference on Pattern Recognition-
dc.titleStructured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICPR.2018.8545862-
dc.identifier.scopuseid_2-s2.0-85059781239-
dc.identifier.volume2018-August-
dc.identifier.spage459-
dc.identifier.epage464-
dc.identifier.issnl1051-4651-

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