File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

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

TitleSubspace segmentation with a minimal squared frobenius norm representation
Authors
Issue Date2012
PublisherIEEE. The Journal's web site is located at https://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 Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 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. © 2012 ICPR Org Committee.
Persistent Identifierhttp://hdl.handle.net/10722/186491
ISBN
ISSN
2023 SCImago Journal Rankings: 0.584
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWei, S-
dc.contributor.authorYu, Y-
dc.date.accessioned2013-08-20T12:11:13Z-
dc.date.available2013-08-20T12:11:13Z-
dc.date.issued2012-
dc.identifier.citationThe 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11-15 November 2012. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, p. 3509-3512-
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. © 2012 ICPR Org Committee.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545-
dc.relation.ispartofProceedings of the 21st International Conference on Pattern Recognition (ICPR2012)-
dc.rightsProceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Copyright © IEEE.-
dc.titleSubspace segmentation with a minimal squared frobenius norm representation-
dc.typeConference_Paper-
dc.identifier.emailYu, Y: yzyu@cs.hku.hk-
dc.identifier.authorityYu, Y=rp01415-
dc.identifier.scopuseid_2-s2.0-84874559203-
dc.identifier.hkuros220943-
dc.identifier.spage3509-
dc.identifier.epage3512-
dc.identifier.isiWOS:000343660603142-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130830-
dc.identifier.issnl1051-4651-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats