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Conference Paper: Subspace segmentation with a minimal square frobenius norm representation

TitleSubspace segmentation with a minimal square frobenius norm representation
Authors
Issue Date2012
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545
Citation
The 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11-15 November 2012. In International Conference on Pattern Recognition, 2012, p. 3509-3512 How to Cite?
AbstractWe introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets, MSFNR is much faster than most state-of-the-art methods while achieving comparable segmentation accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/186491
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWei, Sen_US
dc.contributor.authorYu, Yen_US
dc.date.accessioned2013-08-20T12:11:13Z-
dc.date.available2013-08-20T12:11:13Z-
dc.date.issued2012en_US
dc.identifier.citationThe 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11-15 November 2012. In International Conference on Pattern Recognition, 2012, p. 3509-3512en_US
dc.identifier.isbn978-4-9906441-0-9-
dc.identifier.issn1051-4651-
dc.identifier.urihttp://hdl.handle.net/10722/186491-
dc.description.abstractWe introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets, MSFNR is much faster than most state-of-the-art methods while achieving comparable segmentation accuracy.-
dc.languageengen_US
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545-
dc.relation.ispartofInternational Conference on Pattern Recognitionen_US
dc.rightsInternational Conference on Pattern Recognition. Copyright © IEEE, Computer Society.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSubspace segmentation with a minimal square frobenius norm representationen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y: yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros220943en_US
dc.identifier.spage3509en_US
dc.identifier.epage3512en_US
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130830-

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