Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering

Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously dec...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on circuits and systems for video technology Jg. 32; H. 1; S. 92 - 104
Hauptverfasser:	Chen, Yongyong, Xiao, Xiaolin, Peng, Chong, Lu, Guangming, Zhou, Yicong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Affinity Algorithms Clustering Clustering algorithms Clustering methods Correlation graph learning Learning low-rank Matrix decomposition Multi-view clustering Optimization Outliers (statistics) Representations Sparse matrices Subspace methods Subspaces tensor approximation Tensors
ISSN:	1051-8215, 1558-2205
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously decrease; (2) subspace clustering methods use a "two-step" strategy to learn the representation and affinity matrix independently, and thus may fail to explore their high correlation. To address these issues, we propose a novel multi-view clustering method via learning a L ow- R ank T ensor G raph (LRTG). Different from subspace clustering methods, LRTG simultaneously learns the representation and affinity matrix in a single step to preserve their correlation. We apply Tucker decomposition and <inline-formula> <tex-math notation="LaTeX">l_{2,1} </tex-math></inline-formula>-norm to the LRTG model to alleviate noise and outliers for learning a "clean" representation. LRTG then learns the affinity matrix from this "clean" representation. Additionally, an adaptive neighbor scheme is proposed to find the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> largest entries of the affinity matrix to form a flexible graph for clustering. An effective optimization algorithm is designed to solve the LRTG model based on the alternating direction method of multipliers. Extensive experiments on different clustering tasks demonstrate the effectiveness and superiority of LRTG over seventeen state-of-the-art clustering methods.
AbstractList	Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously decrease; (2) subspace clustering methods use a "two-step" strategy to learn the representation and affinity matrix independently, and thus may fail to explore their high correlation. To address these issues, we propose a novel multi-view clustering method via learning a L ow- R ank T ensor G raph (LRTG). Different from subspace clustering methods, LRTG simultaneously learns the representation and affinity matrix in a single step to preserve their correlation. We apply Tucker decomposition and <inline-formula> <tex-math notation="LaTeX">l_{2,1} </tex-math></inline-formula>-norm to the LRTG model to alleviate noise and outliers for learning a "clean" representation. LRTG then learns the affinity matrix from this "clean" representation. Additionally, an adaptive neighbor scheme is proposed to find the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> largest entries of the affinity matrix to form a flexible graph for clustering. An effective optimization algorithm is designed to solve the LRTG model based on the alternating direction method of multipliers. Extensive experiments on different clustering tasks demonstrate the effectiveness and superiority of LRTG over seventeen state-of-the-art clustering methods. Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously decrease; (2) subspace clustering methods use a “two-step” strategy to learn the representation and affinity matrix independently, and thus may fail to explore their high correlation. To address these issues, we propose a novel multi-view clustering method via learning a L ow- R ank T ensor G raph (LRTG). Different from subspace clustering methods, LRTG simultaneously learns the representation and affinity matrix in a single step to preserve their correlation. We apply Tucker decomposition and [Formula Omitted]-norm to the LRTG model to alleviate noise and outliers for learning a “clean” representation. LRTG then learns the affinity matrix from this “clean” representation. Additionally, an adaptive neighbor scheme is proposed to find the [Formula Omitted] largest entries of the affinity matrix to form a flexible graph for clustering. An effective optimization algorithm is designed to solve the LRTG model based on the alternating direction method of multipliers. Extensive experiments on different clustering tasks demonstrate the effectiveness and superiority of LRTG over seventeen state-of-the-art clustering methods.
Author	Peng, Chong Zhou, Yicong Lu, Guangming Chen, Yongyong Xiao, Xiaolin
Author_xml	– sequence: 1 givenname: Yongyong orcidid: 0000-0003-1970-1993 surname: Chen fullname: Chen, Yongyong email: yongyongchen.cn@gmail.com organization: Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China – sequence: 2 givenname: Xiaolin orcidid: 0000-0001-8827-3991 surname: Xiao fullname: Xiao, Xiaolin email: shellyxiaolin@gmail.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Chong orcidid: 0000-0003-0003-5126 surname: Peng fullname: Peng, Chong email: pchong1991@163.com organization: College of Computer Science and Technology, Qingdao University, Qingdao, China – sequence: 4 givenname: Guangming orcidid: 0000-0003-1578-2634 surname: Lu fullname: Lu, Guangming email: luguangm@hit.edu.cn organization: Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China – sequence: 5 givenname: Yicong orcidid: 0000-0002-4487-6384 surname: Zhou fullname: Zhou, Yicong email: yicongzhou@um.edu.mo organization: Department of Computer and Information Science, University of Macau, Macau, China
BookMark	eNp9kDFPwzAQhS1UJNrCH4AlEnOK7eTieKwiKEhBSDR0tZzEAZfgBDtRxb_HpRUDA8vd6fS-u6c3QxPTGYXQJcELQjC_KbL1plhQTMkiwgAJhRM0JQBpSCmGiZ8xkDClBM7QzLktxiROYzZFy7zbhc_SvAeFMq6zwcrK_i3IlbRGm9eg8avHsR10uNFqF6zH0vWyUkHWjm5Q1kvO0WkjW6cujn2OXu5ui-w-zJ9WD9kyDyvKYQgbTkgNitW-liyRNWZUUdakFZdYljFXPGHSewfagHdbSapAxlBGMSvTuo7m6Ppwt7fd56jcILbdaI1_KWhCOKYRS8Gr0oOqsp1zVjWi0oMcdGcGK3UrCBb7wMRPYGIfmDgG5lH6B-2t_pD263_o6gBppdQvwKMYWIyjb9YYeBA
CODEN	ITCTEM
CitedBy_id	crossref_primary_10_1016_j_inffus_2025_103322 crossref_primary_10_1016_j_sigpro_2022_108901 crossref_primary_10_1109_TCSVT_2024_3401134 crossref_primary_10_1109_TMM_2021_3112230 crossref_primary_10_1007_s00530_023_01234_3 crossref_primary_10_1109_TCSVT_2024_3393148 crossref_primary_10_1016_j_patcog_2022_109264 crossref_primary_10_1109_TKDE_2025_3568795 crossref_primary_10_1109_TNNLS_2025_3545465 crossref_primary_10_1016_j_neucom_2024_128264 crossref_primary_10_1016_j_ins_2024_121575 crossref_primary_10_1109_TIP_2022_3171421 crossref_primary_10_1109_TETCI_2024_3502459 crossref_primary_10_1145_3532612 crossref_primary_10_1109_TCSVT_2023_3302326 crossref_primary_10_1016_j_inffus_2023_02_013 crossref_primary_10_1016_j_phycom_2023_102231 crossref_primary_10_1109_TMM_2022_3185886 crossref_primary_10_1109_TCSVT_2023_3266801 crossref_primary_10_1016_j_inffus_2025_103313 crossref_primary_10_1016_j_engappai_2022_105541 crossref_primary_10_1109_TCSVT_2023_3316279 crossref_primary_10_1016_j_ins_2023_120085 crossref_primary_10_1016_j_neucom_2025_130290 crossref_primary_10_1007_s10489_023_04744_9 crossref_primary_10_1016_j_knosys_2025_113523 crossref_primary_10_1109_TMM_2022_3207922 crossref_primary_10_3390_electronics13112181 crossref_primary_10_1016_j_patcog_2024_110598 crossref_primary_10_1109_TCSVT_2021_3119956 crossref_primary_10_1109_TCSVT_2022_3143848 crossref_primary_10_1016_j_knosys_2022_109127 crossref_primary_10_1016_j_eswa_2025_128007 crossref_primary_10_1016_j_patcog_2024_110473 crossref_primary_10_1109_TCSVT_2023_3312979 crossref_primary_10_1109_TCSVT_2025_3543977 crossref_primary_10_1109_TCSVT_2023_3276362 crossref_primary_10_1109_TCSS_2024_3479188 crossref_primary_10_1109_TCSVT_2021_3089336 crossref_primary_10_1109_TCE_2023_3341908 crossref_primary_10_26599_BDMA_2023_9020004 crossref_primary_10_1109_TCSVT_2023_3241172 crossref_primary_10_1007_s10489_024_05616_6 crossref_primary_10_1016_j_ins_2024_121278 crossref_primary_10_1016_j_patcog_2024_110592 crossref_primary_10_1109_TGRS_2023_3321789 crossref_primary_10_1016_j_eswa_2024_124683 crossref_primary_10_1109_TKDE_2023_3305624 crossref_primary_10_1109_TGRS_2022_3227735 crossref_primary_10_1109_TNNLS_2021_3059874 crossref_primary_10_1109_TCSVT_2022_3201822 crossref_primary_10_1016_j_inffus_2024_102347 crossref_primary_10_1016_j_patcog_2024_110364 crossref_primary_10_1109_TCSVT_2023_3247600 crossref_primary_10_1109_TKDE_2022_3185126 crossref_primary_10_3390_rs17152647 crossref_primary_10_1007_s10489_023_04668_4 crossref_primary_10_1109_TKDE_2024_3391627 crossref_primary_10_1109_TNNLS_2025_3526590 crossref_primary_10_1016_j_knosys_2025_114356 crossref_primary_10_1016_j_knosys_2025_114510 crossref_primary_10_1109_TNNLS_2024_3401449 crossref_primary_10_1016_j_ins_2025_122678 crossref_primary_10_1109_TSIPN_2023_3306098 crossref_primary_10_1109_TCSVT_2022_3200451 crossref_primary_10_1007_s11280_022_01012_7 crossref_primary_10_1109_TCSVT_2022_3207484 crossref_primary_10_1016_j_eswa_2024_124103 crossref_primary_10_1109_TCSVT_2024_3396332 crossref_primary_10_1007_s10489_023_05181_4 crossref_primary_10_1109_TSC_2023_3241659 crossref_primary_10_1109_TCSVT_2024_3376720 crossref_primary_10_1109_TCSVT_2024_3375997 crossref_primary_10_1016_j_eswa_2023_121518 crossref_primary_10_1016_j_inffus_2024_102709 crossref_primary_10_1109_TBDATA_2022_3163584 crossref_primary_10_1109_TCSVT_2023_3275299 crossref_primary_10_1016_j_neucom_2023_126320 crossref_primary_10_1109_TAI_2023_3293479 crossref_primary_10_1007_s00500_023_07919_1 crossref_primary_10_1145_3568684 crossref_primary_10_1109_TCSVT_2023_3249796 crossref_primary_10_1109_TGRS_2023_3285269 crossref_primary_10_1109_TETCI_2024_3353037 crossref_primary_10_1109_TCSVT_2022_3162575 crossref_primary_10_1109_TIP_2024_3378471 crossref_primary_10_1109_TCSVT_2023_3263853 crossref_primary_10_1145_3612923 crossref_primary_10_1109_TGRS_2024_3370633 crossref_primary_10_1109_TKDE_2025_3555043 crossref_primary_10_1016_j_patcog_2025_111818 crossref_primary_10_1109_TSP_2021_3101979 crossref_primary_10_1093_comjnl_bxae049 crossref_primary_10_1109_TCSVT_2024_3442295 crossref_primary_10_1016_j_neucom_2022_09_145 crossref_primary_10_1145_3735644 crossref_primary_10_1109_TCSVT_2021_3089480 crossref_primary_10_1109_TCSVT_2023_3275985 crossref_primary_10_1109_TCSVT_2023_3266283 crossref_primary_10_1109_TNNLS_2025_3545435 crossref_primary_10_1016_j_neunet_2025_107652 crossref_primary_10_1145_3645108 crossref_primary_10_1016_j_ins_2023_119570 crossref_primary_10_1109_TCSVT_2022_3229356 crossref_primary_10_1109_TCSVT_2024_3430041 crossref_primary_10_1109_TMM_2023_3307239 crossref_primary_10_1007_s10489_022_04074_2 crossref_primary_10_1109_TIP_2023_3340609 crossref_primary_10_1109_TSIPN_2022_3169633 crossref_primary_10_1109_TCSVT_2023_3299318 crossref_primary_10_1109_TCSVT_2022_3197230 crossref_primary_10_1109_TII_2022_3197201 crossref_primary_10_1109_TNNLS_2022_3202102 crossref_primary_10_1109_TNSE_2024_3485646 crossref_primary_10_1007_s10489_025_06814_6 crossref_primary_10_1109_TIFS_2023_3337717 crossref_primary_10_1109_TCYB_2024_3443198 crossref_primary_10_1109_TNSE_2023_3244624 crossref_primary_10_1016_j_knosys_2024_112106 crossref_primary_10_1109_TNNLS_2024_3356228 crossref_primary_10_1109_TCSVT_2023_3306811 crossref_primary_10_3390_math11061509 crossref_primary_10_1109_TCSVT_2024_3509892 crossref_primary_10_1109_TCYB_2023_3263175 crossref_primary_10_1016_j_neucom_2024_128093 crossref_primary_10_1109_TIP_2021_3068646 crossref_primary_10_1109_TIP_2023_3293764 crossref_primary_10_1109_TIP_2025_3583122 crossref_primary_10_1109_TNNLS_2025_3527120 crossref_primary_10_1007_s00521_023_08279_5 crossref_primary_10_1109_TCSVT_2023_3258926 crossref_primary_10_1007_s10462_025_11240_8 crossref_primary_10_1016_j_neunet_2023_02_016 crossref_primary_10_1109_TCSVT_2021_3127007 crossref_primary_10_1007_s10489_022_03406_6 crossref_primary_10_1016_j_neucom_2025_129766 crossref_primary_10_1016_j_inffus_2023_101941 crossref_primary_10_1109_ACCESS_2022_3176436 crossref_primary_10_1109_TCSVT_2024_3492814 crossref_primary_10_1109_TCSVT_2024_3399596 crossref_primary_10_1016_j_neunet_2025_108003 crossref_primary_10_1117_1_JRS_18_046510 crossref_primary_10_1007_s11042_023_16846_0 crossref_primary_10_1109_TMM_2023_3347639
Cites_doi	10.1109/TCYB.2020.2987164 10.1109/CVPR.2015.7298657 10.1109/TIP.2019.2916740 10.1109/TPAMI.2018.2847335 10.1109/TIP.2017.2754939 10.1109/JSTSP.2018.2873148 10.1109/TIP.2016.2627806 10.1109/TCYB.2018.2869789 10.1145/2623330.2623726 10.1016/j.patcog.2020.107307 10.1109/TIP.2010.2049235 10.1109/TIP.2019.2941319 10.1109/TPAMI.2012.88 10.1109/JSTSP.2018.2875385 10.1109/TPAMI.2013.57 10.1109/CVPR.2014.484 10.1109/TPAMI.2018.2877660 10.1109/TIP.2018.2825647 10.1145/3343031.3351023 10.1109/ICCV.2011.6126528 10.1109/TCYB.2018.2887094 10.1609/aaai.v34i04.6109 10.1109/ICDM.2016.0167 10.1017/CBO9780511804441 10.1007/s00521-019-04688-7 10.1109/TCYB.2017.2751646 10.1137/07070111X 10.1016/j.neucom.2019.01.004 10.1109/CVPR.2019.00798 10.1561/2200000016 10.1109/ICCV.2019.00826 10.1126/science.290.5500.2323 10.1016/j.knosys.2020.105482 10.1109/TGRS.2017.2706326 10.1109/TPAMI.2018.2794348 10.1109/TCYB.2016.2536752 10.1109/TIP.2017.2754942 10.1109/LSP.2018.2791606 10.1109/TKDE.2019.2903810 10.1109/ICME.2019.00234 10.24963/ijcai.2017/357 10.1109/TPAMI.2015.2462360 10.1109/TSIPN.2020.2988572 10.1162/089976603321780317 10.1007/s11263-018-1086-2 10.1109/ICCV.2015.185 10.1109/ICDM.2019.00095 10.1109/TMM.2019.2952984 10.1109/SSP.2016.7551734 10.1609/aaai.v34i04.6146 10.1109/TPAMI.2020.3037734 10.1109/TCSVT.2020.2967424 10.1109/CVPR.2017.8 10.1109/TNNLS.2018.2851444 10.1109/MSP.2010.939739 10.1016/j.patcog.2020.107441 10.1137/130905010
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TCSVT.2021.3055625
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-2205
EndPage	104
ExternalDocumentID	10_1109_TCSVT_2021_3055625 9345740
Genre	orig-research
GrantInformation_xml	– fundername: University of Macau grantid: MYRG2018-00136-FST funderid: 10.13039/501100004733 – fundername: Science and Technology Development Fund, Macau SAR grantid: 189/2017/A3 funderid: 10.13039/501100006469
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ H~9 ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS RXW TAE TN5 VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c295t-f911d5e7d1d5b76ad072e27f8c9a0ab49e967a05552f5105ca2e5a45b347b8dd3
IEDL.DBID	RIE
ISICitedReferencesCount	165
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000742183600012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1051-8215
IngestDate	Sun Jun 29 15:40:08 EDT 2025 Tue Nov 18 22:16:48 EST 2025 Sat Nov 29 01:44:15 EST 2025 Wed Aug 27 03:03:14 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c295t-f911d5e7d1d5b76ad072e27f8c9a0ab49e967a05552f5105ca2e5a45b347b8dd3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-1970-1993 0000-0001-8827-3991 0000-0002-4487-6384 0000-0003-0003-5126 0000-0003-1578-2634
PQID	2619023785
PQPubID	85433
PageCount	13
ParticipantIDs	crossref_citationtrail_10_1109_TCSVT_2021_3055625 proquest_journals_2619023785 crossref_primary_10_1109_TCSVT_2021_3055625 ieee_primary_9345740
PublicationCentury	2000
PublicationDate	2022-Jan. 2022-1-00 20220101
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– month: 01 year: 2022 text: 2022-Jan.
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on circuits and systems for video technology
PublicationTitleAbbrev	TCSVT
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref57 ref13 ref56 ref59 ref15 ref58 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 roweis (ref35) 2000; 290 ref42 guo (ref33) 2015 xia (ref20) 2014 ref43 ref49 ref8 ref7 ref9 ref4 ref3 jia (ref47) 2020 ref6 ref5 teng (ref14) 2020 ref40 ref34 ref37 ref36 lei (ref12) 2020 ref31 ref30 ref32 ref1 ref39 xing (ref2) 2020 xie (ref53) 2020 ref38 jia (ref41) 2020 ref24 ref23 ref26 ref25 xie (ref44) 2020 ref64 ref63 ref66 ref22 ref65 ref21 ref28 ref27 ref29 ref60 ref62 ref61
References_xml	– ident: ref31 doi: 10.1109/TCYB.2020.2987164 – ident: ref23 doi: 10.1109/CVPR.2015.7298657 – ident: ref18 doi: 10.1109/TIP.2019.2916740 – ident: ref30 doi: 10.1109/TPAMI.2018.2847335 – ident: ref19 doi: 10.1109/TIP.2017.2754939 – ident: ref56 doi: 10.1109/JSTSP.2018.2873148 – ident: ref9 doi: 10.1109/TIP.2016.2627806 – ident: ref11 doi: 10.1109/TCYB.2018.2869789 – ident: ref27 doi: 10.1145/2623330.2623726 – ident: ref32 doi: 10.1016/j.patcog.2020.107307 – ident: ref36 doi: 10.1109/TIP.2010.2049235 – ident: ref63 doi: 10.1109/TIP.2019.2941319 – ident: ref6 doi: 10.1109/TPAMI.2012.88 – ident: ref48 doi: 10.1109/JSTSP.2018.2875385 – ident: ref5 doi: 10.1109/TPAMI.2013.57 – ident: ref7 doi: 10.1109/CVPR.2014.484 – year: 2020 ident: ref14 article-title: Multi-view spatial attention embedding for vehicle re-identification publication-title: IEEE Trans Circuits Syst Video Technol – ident: ref49 doi: 10.1109/TPAMI.2018.2877660 – ident: ref24 doi: 10.1109/TIP.2018.2825647 – ident: ref4 doi: 10.1145/3343031.3351023 – ident: ref64 doi: 10.1109/ICCV.2011.6126528 – ident: ref28 doi: 10.1109/TCYB.2018.2887094 – ident: ref66 doi: 10.1609/aaai.v34i04.6109 – ident: ref65 doi: 10.1109/ICDM.2016.0167 – ident: ref61 doi: 10.1017/CBO9780511804441 – ident: ref39 doi: 10.1007/s00521-019-04688-7 – ident: ref26 doi: 10.1109/TCYB.2017.2751646 – ident: ref60 doi: 10.1137/07070111X – ident: ref46 doi: 10.1016/j.neucom.2019.01.004 – ident: ref13 doi: 10.1109/CVPR.2019.00798 – ident: ref59 doi: 10.1561/2200000016 – ident: ref50 doi: 10.1109/ICCV.2019.00826 – volume: 290 start-page: 2323 year: 2000 ident: ref35 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science doi: 10.1126/science.290.5500.2323 – ident: ref37 doi: 10.1016/j.knosys.2020.105482 – ident: ref55 doi: 10.1109/TGRS.2017.2706326 – ident: ref8 doi: 10.1109/TPAMI.2018.2794348 – ident: ref62 doi: 10.1109/TCYB.2016.2536752 – year: 2020 ident: ref44 article-title: Robust kernelized multiview self-representation for subspace clustering publication-title: IEEE Trans Neural Netw Learn Syst – ident: ref10 doi: 10.1109/TIP.2017.2754942 – ident: ref42 doi: 10.1109/LSP.2018.2791606 – ident: ref21 doi: 10.1109/TKDE.2019.2903810 – ident: ref15 doi: 10.1109/ICME.2019.00234 – ident: ref25 doi: 10.24963/ijcai.2017/357 – ident: ref57 doi: 10.1109/TPAMI.2015.2462360 – year: 2020 ident: ref41 article-title: Constrained clustering with dissimilarity propagation-guided graph-Laplacian PCA publication-title: IEEE Trans Neural Netw Learn Syst – year: 2020 ident: ref53 article-title: Joint deep multi-view learning for image clustering publication-title: IEEE Trans Knowl Data Eng – ident: ref40 doi: 10.1109/TSIPN.2020.2988572 – ident: ref34 doi: 10.1162/089976603321780317 – ident: ref17 doi: 10.1007/s11263-018-1086-2 – ident: ref16 doi: 10.1109/ICCV.2015.185 – ident: ref38 doi: 10.1109/ICDM.2019.00095 – ident: ref43 doi: 10.1109/TMM.2019.2952984 – ident: ref29 doi: 10.1109/SSP.2016.7551734 – ident: ref52 doi: 10.1609/aaai.v34i04.6146 – ident: ref51 doi: 10.1109/TPAMI.2020.3037734 – year: 2020 ident: ref47 article-title: Multi-view spectral clustering tailored tensor low-rank representation publication-title: arXiv 2004 14705 – ident: ref3 doi: 10.1109/TCSVT.2020.2967424 – year: 2020 ident: ref2 article-title: Robust high-order manifold constrained low rank representation for subspace clustering publication-title: IEEE Trans Circuits Syst Video Technol – year: 2020 ident: ref12 article-title: Deep spatial-spectral subspace clustering for hyperspectral image publication-title: IEEE Trans Circuits Syst Video Technol – ident: ref22 doi: 10.1109/CVPR.2017.8 – ident: ref45 doi: 10.1109/TNNLS.2018.2851444 – ident: ref1 doi: 10.1109/MSP.2010.939739 – ident: ref58 doi: 10.1016/j.patcog.2020.107441 – start-page: 2149 year: 2014 ident: ref20 article-title: Robust multi-view spectral clustering via low-rank and sparse decomposition publication-title: Proc AAAI – start-page: 3547 year: 2015 ident: ref33 article-title: Robust subspace segmentation by simultaneously learning data representations and their affinity matrix publication-title: Proc Int Joint Conf Artif Intell – ident: ref54 doi: 10.1137/130905010
SSID	ssj0014847
Score	2.6807244
Snippet	Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	92
SubjectTerms	Adaptation models Affinity Algorithms Clustering Clustering algorithms Clustering methods Correlation graph learning Learning low-rank Matrix decomposition Multi-view clustering Optimization Outliers (statistics) Representations Sparse matrices Subspace methods Subspaces tensor approximation Tensors
Title	Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering
URI	https://ieeexplore.ieee.org/document/9345740 https://www.proquest.com/docview/2619023785
Volume	32
WOSCitedRecordID	wos000742183600012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-2205 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014847 issn: 1051-8215 databaseCode: RIE dateStart: 19910101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFA9zeNCDX1OcTsnBm2br0qZpjmM4PcgQrWO3kiapDEcr-3D_vnlZVwaK4KXkkED5vXy830ve7yF0k9o5kTJNSSoiBdGqkERKSCKzwJfdSFm7aFdsgg-H0XgsnmvorsqFMca4x2emDU13l68LtYRQWUf4AeOBJeg7nPN1rlZ1YxBErpiYdRe6JLLn2CZBxhOduP86ii0VpN22E8SCsthbh5CrqvJjK3bny-Dwf392hA5KPxL31oY_RjWTn6D9LXXBBuo9FSvyIvMPHFuuWszwA4hT41JR9R1bdxW7_FsympgVhi3EEmiD-9MlqCfYLqfobXAf9x9JWTGBKCrYgmR269LMcG2_KQ-l9jg1lGcAvyfTQBgRcgkaXzQDz0pJapgMWOoHPI209s9QPS9yc46wgfck0rf8Iw0DbZtKKcM45KFKlvleE3U3ECaqlBOHqhbTxNEKTyQO9gRgT0rYm-i2GvO5FtP4s3cDgK56lhg3UWtjqaRcb_MEeKD1PnjELn4fdYn2KCQuuOBJC9UXs6W5QrvqazGZz67dVPoGyiXFyw
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5EBfXgW6xWzcGbRnezSbM5luILaxFdxduSTbJSlFZqq3_fTLotgiJ4WXJIYJlJZuabZL4BOCz8niiEZbRQqcFsVYOmRmmqS57oODVeLzY0m5CdTvr0pG5n4HhaC-OcC4_P3AkOw12-7ZsRpspOVcKF5B6gzwnOWTyu1preGfA0tBPzAUNMU-_JJiUykTrNWvePmQeDLD4JlFjYGPubGwp9VX4Y4-Bhzlf-92-rsFxFkqQ5Vv0azLjeOix94xfcgGa7_0nvdO-FZB6t9gfkAumpScWp-kx8wEpCBS597LpPgkbEQ2hHWq8j5E_wUzbh4fwsa13SqmcCNUyJIS298bLCSeu_hWxoG0nmmCxRAZEuuHKqITWyfLESYyujmROaiyLhskitTbZgttfvuW0gDl-U6MQjkKLBrR8aY5yQWImqRZlENYgnIsxNRSiOfS1e8wAsIpUHseco9rwSew2OpmvexnQaf87eQEFPZ1YyrkF9oqm8OnHvOSJBH3_IVOz8vuoAFi6zm3bevupc78IiwzKGkEqpw-xwMHJ7MG8-ht33wX7YVl9AC8kS
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Low-Rank+Tensor+Graph+Learning+for+Multi-View+Subspace+Clustering&rft.jtitle=IEEE+transactions+on+circuits+and+systems+for+video+technology&rft.au=Chen%2C+Yongyong&rft.au=Xiao%2C+Xiaolin&rft.au=Chong%2C+Peng&rft.au=Lu%2C+Guangming&rft.date=2022-01-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=1051-8215&rft.eissn=1558-2205&rft.volume=32&rft.issue=1&rft.spage=92&rft_id=info:doi/10.1109%2FTCSVT.2021.3055625&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1051-8215&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1051-8215&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1051-8215&client=summon