Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering
•An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is proposed.•The T-SNE visualizations of the features learned by the proposed Deep-NMF and its counterpart ascertain the effectiveness of the pr...
Uloženo v:
| Vydáno v: | Pattern recognition Ročník 131; s. 108815 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.11.2022
|
| Témata: | |
| ISSN: | 0031-3203, 1873-5142 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is proposed.•The T-SNE visualizations of the features learned by the proposed Deep-NMF and its counterpart ascertain the effectiveness of the proposed framework for multi-view clustering.•The proposed Deep-NMF method learns and incorporates the most consensed manifold for multi-view data in all layers of the multi-layer architecture.•The objective function is designed to uncover the consensus representation that is unique and encodes both the view-shared, view-specific information for multi-view data.•Extensive experiments including features visualization, components-based and multi-layer ability analysis, comprehensive examples have been conducted and presented in this work.
Multi-view data clustering based on Non-negative Matrix Factorization (NMF) has been commonly used for pattern recognition by grouping multi-view high-dimensional data by projecting it to a lower-order dimensional space. However, the NMF framework fails to learn the accurate lower-order representation of the input data if it exhibits complex and non-linear relationships. This paper proposes a deep non-negative matrix factorization-based framework for effective multi-view data clustering by uncovering both the non-linear relationships and the intrinsic components of the data. Both the consensus and complementary information present in multiple views are sufficiently learned in the proposed framework with the effective use of constraints such as normalized cut-type and orthogonal. The optimal manifold of multi-view data is effectively incorporated in all layers of the framework. Extensive experimental results show the proposed method outperforms state-of-the-art multi-view matrix factorization-based methods. |
|---|---|
| AbstractList | •An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is proposed.•The T-SNE visualizations of the features learned by the proposed Deep-NMF and its counterpart ascertain the effectiveness of the proposed framework for multi-view clustering.•The proposed Deep-NMF method learns and incorporates the most consensed manifold for multi-view data in all layers of the multi-layer architecture.•The objective function is designed to uncover the consensus representation that is unique and encodes both the view-shared, view-specific information for multi-view data.•Extensive experiments including features visualization, components-based and multi-layer ability analysis, comprehensive examples have been conducted and presented in this work.
Multi-view data clustering based on Non-negative Matrix Factorization (NMF) has been commonly used for pattern recognition by grouping multi-view high-dimensional data by projecting it to a lower-order dimensional space. However, the NMF framework fails to learn the accurate lower-order representation of the input data if it exhibits complex and non-linear relationships. This paper proposes a deep non-negative matrix factorization-based framework for effective multi-view data clustering by uncovering both the non-linear relationships and the intrinsic components of the data. Both the consensus and complementary information present in multiple views are sufficiently learned in the proposed framework with the effective use of constraints such as normalized cut-type and orthogonal. The optimal manifold of multi-view data is effectively incorporated in all layers of the framework. Extensive experimental results show the proposed method outperforms state-of-the-art multi-view matrix factorization-based methods. |
| ArticleNumber | 108815 |
| Author | Luong, Khanh Nayak, Richi Bashar, Md Abul Balasubramaniam, Thirunavukarasu |
| Author_xml | – sequence: 1 givenname: Khanh orcidid: 0000-0001-6981-7367 surname: Luong fullname: Luong, Khanh email: khanh.luong@qut.edu.au – sequence: 2 givenname: Richi surname: Nayak fullname: Nayak, Richi email: r.nayak@qut.edu.au – sequence: 3 givenname: Thirunavukarasu orcidid: 0000-0002-8821-6003 surname: Balasubramaniam fullname: Balasubramaniam, Thirunavukarasu email: thirunavukarasu.balas@qut.edu.au – sequence: 4 givenname: Md Abul surname: Bashar fullname: Bashar, Md Abul email: m1.bashar@qut.edu.au |
| BookMark | eNqFUMtOwzAQtBBItIU_4OAfSPHaSZxyQEIVL6mIC5wtx95UrtKkst1C-XrchhMHuOxKMzuzmhmT067vkJArYFNgUF6vphsdTb-ccsZ5gqoKihMygkqKrICcn5IRYwIywZk4J-MQVoyBTMSI2JdtG13W6j16utada_rW0ha171y3pE3vqUXc0PQx63Cpo9thuovefdJGm9h795XAxNY6oKXro93O4Qc17TZE9Mnmgpw1ug14-bMn5P3h_m3-lC1eH5_nd4vMCFbGrOKSl6yoZVFXrISmqCpW5DOEGdQ5SCvAytKINJBrA1rPZCkklNbwXGppxITkg6_xfQgeG7Xxbq39XgFTh6bUSg1NqUNTamgqyW5-yYyLx1DRa9f-J74dxJiCpdxeBeOwM2idRxOV7d3fBt_R7oo3 |
| CitedBy_id | crossref_primary_10_1016_j_patcog_2023_109860 crossref_primary_10_1109_TSIPN_2024_3511262 crossref_primary_10_1016_j_patcog_2023_110179 crossref_primary_10_1016_j_patcog_2022_109102 crossref_primary_10_1007_s00371_024_03661_3 crossref_primary_10_1016_j_is_2024_102379 crossref_primary_10_1016_j_engappai_2024_107978 crossref_primary_10_1016_j_eswa_2023_121518 crossref_primary_10_1016_j_engappai_2025_110661 crossref_primary_10_1016_j_patcog_2024_111140 crossref_primary_10_1016_j_eswa_2025_129556 crossref_primary_10_1007_s10489_024_05652_2 crossref_primary_10_1016_j_ins_2024_120769 crossref_primary_10_1016_j_envres_2023_117355 crossref_primary_10_1016_j_dsp_2024_104713 crossref_primary_10_1109_TCE_2023_3319018 crossref_primary_10_1109_TCSVT_2024_3508785 crossref_primary_10_1016_j_eswa_2024_123645 crossref_primary_10_1016_j_knosys_2022_109736 crossref_primary_10_1145_3767726 crossref_primary_10_1007_s13042_025_02589_x crossref_primary_10_1109_TNNLS_2023_3304626 crossref_primary_10_1016_j_engappai_2024_109508 crossref_primary_10_1007_s10489_025_06367_8 crossref_primary_10_1109_TNNLS_2025_3551159 crossref_primary_10_1016_j_jhydrol_2025_132892 crossref_primary_10_1016_j_neucom_2024_128594 crossref_primary_10_1007_s00138_023_01455_6 crossref_primary_10_1016_j_engappai_2025_110715 crossref_primary_10_1016_j_ins_2024_120458 crossref_primary_10_1007_s10044_025_01455_4 crossref_primary_10_1007_s10489_024_05870_8 crossref_primary_10_1016_j_eswa_2024_123831 crossref_primary_10_1016_j_patcog_2023_109963 crossref_primary_10_1109_TCE_2025_3525523 crossref_primary_10_1007_s12206_025_0808_y crossref_primary_10_1016_j_trc_2024_104607 crossref_primary_10_1007_s11075_025_02147_0 crossref_primary_10_1016_j_cosrev_2025_100788 crossref_primary_10_1016_j_neucom_2024_128367 crossref_primary_10_1016_j_neucom_2024_127555 crossref_primary_10_26599_BDMA_2023_9020004 crossref_primary_10_1016_j_ins_2024_120585 crossref_primary_10_1016_j_patcog_2025_112011 crossref_primary_10_1109_ACCESS_2023_3285662 crossref_primary_10_1016_j_knosys_2024_112662 crossref_primary_10_1007_s10489_023_04716_z crossref_primary_10_1016_j_knosys_2023_111330 crossref_primary_10_1016_j_dsp_2024_104534 crossref_primary_10_1109_TBDATA_2025_3547174 crossref_primary_10_1016_j_patcog_2025_111679 crossref_primary_10_1016_j_inffus_2024_102785 crossref_primary_10_3390_math13091422 crossref_primary_10_1016_j_dsp_2023_104118 crossref_primary_10_1016_j_patcog_2024_110645 crossref_primary_10_1109_TETCI_2024_3451352 crossref_primary_10_1109_TIP_2023_3261746 crossref_primary_10_1109_TNNLS_2023_3265699 crossref_primary_10_1109_TCE_2024_3440485 crossref_primary_10_1016_j_knosys_2025_114158 crossref_primary_10_1016_j_neunet_2023_10_001 crossref_primary_10_1016_j_neunet_2024_106602 crossref_primary_10_1016_j_knosys_2025_114357 crossref_primary_10_1016_j_knosys_2025_113349 crossref_primary_10_1016_j_asoc_2023_110702 crossref_primary_10_1109_LSP_2025_3553804 crossref_primary_10_1016_j_patcog_2024_111010 |
| Cites_doi | 10.1038/44565 10.1109/TCYB.2019.2918495 10.1109/TPAMI.2008.277 10.1109/TCYB.2017.2747400 10.1016/j.knosys.2020.105582 10.1371/journal.pone.0208494 10.1007/s10115-004-0194-1 10.1109/TPAMI.2010.231 10.1016/j.patcog.2019.107015 10.1109/TPAMI.2016.2554555 10.1016/j.patcog.2021.107890 10.1016/j.knosys.2021.106807 10.26599/BDMA.2018.9020003 10.3390/technologies9010002 10.1109/34.868688 10.1016/j.neunet.2017.02.003 10.1016/j.neucom.2019.12.054 10.1109/TIP.2006.884956 10.1145/1007730.1007731 |
| ContentType | Journal Article |
| Copyright | 2022 |
| Copyright_xml | – notice: 2022 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.patcog.2022.108815 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-5142 |
| ExternalDocumentID | 10_1016_j_patcog_2022_108815 S0031320322002965 |
| GroupedDBID | --K --M -D8 -DT -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 29O 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFRF ABHFT ABJNI ABMAC ABTAH ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADMXK ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FD6 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM KZ1 LG9 LMP LY1 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SST SSV SSZ T5K TN5 UNMZH VOH WUQ XJE XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c306t-8272605b75b8061f5880549e191b417d31d76c3d76e2ac1aa9763716dc247a7c3 |
| ISICitedReferencesCount | 74 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000866467500004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0031-3203 |
| IngestDate | Sat Nov 29 07:30:13 EST 2025 Tue Nov 18 22:08:46 EST 2025 Fri Feb 23 02:40:09 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Multi-view data/clustering Manifold learning Deep Non-negative Matrix Factorization (Deep-NMF) Non-negative Matrix Factorization (NMF) Deep Matrix Factorization (DMF) |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-8272605b75b8061f5880549e191b417d31d76c3d76e2ac1aa9763716dc247a7c3 |
| ORCID | 0000-0001-6981-7367 0000-0002-8821-6003 |
| ParticipantIDs | crossref_primary_10_1016_j_patcog_2022_108815 crossref_citationtrail_10_1016_j_patcog_2022_108815 elsevier_sciencedirect_doi_10_1016_j_patcog_2022_108815 |
| PublicationCentury | 2000 |
| PublicationDate | November 2022 2022-11-00 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: November 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Pattern recognition |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Wang, Tian, Yu, Liu, Zhan, Wang (bib0028) 2018; 48 Yang, Wang (bib0041) 2018; 1 Zhou, Zhang, Peng, Bhaskar, Yang (bib0027) 2020; 50 Jianbo Shi, Malik (bib0020) 2000; 22 Zhang, Shan, Chen, Gao (bib0049) 2007; 16 Ding, Li, Jordan (bib0017) 2010; 32 van der Maaten, Hinton (bib0051) 2008; 9 Lee, Seung (bib0011) 1999; 401 Cao, Zhang, Fu, Liu, Zhang (bib0024) 2015 Jaiswal, Babu, Zadeh, Banerjee, Makedon (bib0040) 2021; 9 Chen, Kornblith, Norouzi, Hinton (bib0035) 2020 Luong, Nayak (bib0002) 2020 Ding, Li, Peng, Park (bib0044) 2006 Luo, Zhang, Zhang, Cao (bib0025) 2018; vol. 32 Chang, Hu, Li, Wang, Peng (bib0050) 2021; 217 Liu, Teng, Fei, Zhang, Fang, Zhang, Wu (bib0026) 2021; 115 Song, Liu, Huang, Wang, Tan (bib0008) 2013 Liang, Huang, Wang (bib0022) 2019 Schütze, Manning, Raghavan (bib0047) 2008; vol. 39 Nitsche, Tropmann-Frick (bib0039) 2018 Huang, Kang, Xu (bib0001) 2020; 97 Liang, Yang, Li, Sun, Xie (bib0004) 2020; 194 Kumar, Rai, Daume (bib0021) 2011; vol. 24 Ng, Jordan, Weiss (bib0007) 2001 Ross, Zemel (bib0043) 2006; 7 Trigeorgis, Bousmalis, Zafeiriou, Schuller (bib0013) 2016; 39 Boyd, Vandenberghe (bib0045) 2004 Yuan, Lin, Kuen, Zhang, Wang, Maire, Kale, Faieta (bib0038) 2021 Lyu, Xie, Sun (bib0031) 2017 Li, Zhou, Qiu, Wang, Zhang, Xie (bib0042) 2020; 390 Zhong, Ghosh (bib0046) 2005; 8 Luong, Nayak (bib0003) 2018 Andrew, Arora, Bilmes, Livescu (bib0033) 2013; vol. 28 Luong, Balasubramaniam, Nayak (bib0030) 2018 Belkin, Niyogi, Sindhwani (bib0019) 2006; 7 He, Fan, Wu, Xie, Girshick (bib0036) 2020 Jing, Jiawei, Jialu, Chi (bib0010) 2013 Morgado, Vasconcelos, Misra (bib0037) 2021 Wang, Arora, Livescu, Bilmes (bib0034) 2015 Zhao, Ding, Fu (bib0014) 2017 Zong, Zhang, Zhao, Yu, Zhao (bib0029) 2017; 88 Parsons, Haque, Liu (bib0006) 2004; 6 Wei, Wang, Yu, Domeniconi, Zhang (bib0015) 2020 Cui, Yu, Zhang, Li (bib0016) 2019 Lee, Seung (bib0005) 2001 Cai, He, Han, Huang (bib0018) 2011; 33 Li, Nie, Huang, Huang (bib0009) 2015 Zhou, Ye, Du (bib0023) 2018 Hou, Nayak (bib0048) 2015 Trigeorgis, Bousmalis, Zafeiriou, Schuller (bib0012) 2014 Zhao, Xu, Guan, Liu (bib0032) 2020 Wang (10.1016/j.patcog.2022.108815_bib0034) 2015 Boyd (10.1016/j.patcog.2022.108815_bib0045) 2004 Kumar (10.1016/j.patcog.2022.108815_bib0021) 2011; vol. 24 Song (10.1016/j.patcog.2022.108815_bib0008) 2013 Trigeorgis (10.1016/j.patcog.2022.108815_bib0013) 2016; 39 Ding (10.1016/j.patcog.2022.108815_bib0017) 2010; 32 Ross (10.1016/j.patcog.2022.108815_bib0043) 2006; 7 Liu (10.1016/j.patcog.2022.108815_bib0026) 2021; 115 Zong (10.1016/j.patcog.2022.108815_bib0029) 2017; 88 Ding (10.1016/j.patcog.2022.108815_bib0044) 2006 Hou (10.1016/j.patcog.2022.108815_bib0048) 2015 van der Maaten (10.1016/j.patcog.2022.108815_bib0051) 2008; 9 Jianbo Shi (10.1016/j.patcog.2022.108815_bib0020) 2000; 22 Yuan (10.1016/j.patcog.2022.108815_bib0038) 2021 Morgado (10.1016/j.patcog.2022.108815_bib0037) 2021 Chang (10.1016/j.patcog.2022.108815_bib0050) 2021; 217 Trigeorgis (10.1016/j.patcog.2022.108815_bib0012) 2014 Liang (10.1016/j.patcog.2022.108815_bib0022) 2019 Parsons (10.1016/j.patcog.2022.108815_bib0006) 2004; 6 Zhao (10.1016/j.patcog.2022.108815_bib0014) 2017 Jaiswal (10.1016/j.patcog.2022.108815_bib0040) 2021; 9 Ng (10.1016/j.patcog.2022.108815_bib0007) 2001 Zhou (10.1016/j.patcog.2022.108815_bib0023) 2018 Jing (10.1016/j.patcog.2022.108815_bib0010) 2013 Cao (10.1016/j.patcog.2022.108815_bib0024) 2015 Zhong (10.1016/j.patcog.2022.108815_bib0046) 2005; 8 Zhang (10.1016/j.patcog.2022.108815_bib0049) 2007; 16 Schütze (10.1016/j.patcog.2022.108815_bib0047) 2008; vol. 39 Luong (10.1016/j.patcog.2022.108815_bib0030) 2018 Belkin (10.1016/j.patcog.2022.108815_bib0019) 2006; 7 Huang (10.1016/j.patcog.2022.108815_bib0001) 2020; 97 Cui (10.1016/j.patcog.2022.108815_bib0016) 2019 Zhou (10.1016/j.patcog.2022.108815_bib0027) 2020; 50 Lyu (10.1016/j.patcog.2022.108815_bib0031) 2017 Andrew (10.1016/j.patcog.2022.108815_bib0033) 2013; vol. 28 Lee (10.1016/j.patcog.2022.108815_bib0011) 1999; 401 Wang (10.1016/j.patcog.2022.108815_bib0028) 2018; 48 Lee (10.1016/j.patcog.2022.108815_bib0005) 2001 Luong (10.1016/j.patcog.2022.108815_bib0002) 2020 Luong (10.1016/j.patcog.2022.108815_bib0003) 2018 Liang (10.1016/j.patcog.2022.108815_bib0004) 2020; 194 Cai (10.1016/j.patcog.2022.108815_bib0018) 2011; 33 Yang (10.1016/j.patcog.2022.108815_bib0041) 2018; 1 He (10.1016/j.patcog.2022.108815_bib0036) 2020 Zhao (10.1016/j.patcog.2022.108815_bib0032) 2020 Li (10.1016/j.patcog.2022.108815_bib0009) 2015 Nitsche (10.1016/j.patcog.2022.108815_bib0039) 2018 Luo (10.1016/j.patcog.2022.108815_bib0025) 2018; vol. 32 Li (10.1016/j.patcog.2022.108815_bib0042) 2020; 390 Wei (10.1016/j.patcog.2022.108815_bib0015) 2020 Chen (10.1016/j.patcog.2022.108815_bib0035) 2020 |
| References_xml | – volume: 390 start-page: 108 year: 2020 end-page: 116 ident: bib0042 article-title: Deep graph regularized non-negative matrix factorization for multi-view clustering publication-title: Neurocomputing – volume: 401 start-page: 788 year: 1999 end-page: 791 ident: bib0011 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature – start-page: 285 year: 2018 end-page: 300 ident: bib0030 article-title: A novel technique of using coupled matrix and greedy coordinate descent for multi-view data representation publication-title: International Conference on Web Information Systems Engineering – volume: 7 year: 2006 ident: bib0043 article-title: Learning parts-based representations of data publication-title: J. Mach. Learn. Res. – start-page: 252 year: 2013 end-page: 260 ident: bib0010 article-title: Multi-view clustering via joint nonnegative matrix factorization publication-title: SDM – volume: vol. 24 start-page: 1413 year: 2011 end-page: 1421 ident: bib0021 article-title: Co-regularized multi-view spectral clustering publication-title: Advances in Neural Information Processing Systems – start-page: 567 year: 2019 end-page: 582 ident: bib0016 article-title: Self-weighted multi-view clustering with deep matrix factorization publication-title: Asian Conference on Machine Learning – start-page: 865 year: 2020 end-page: 876 ident: bib0002 article-title: A novel approach to learning consensus and complementary information for multi-view data clustering publication-title: 2020 IEEE 36th International Conference on Data Engineering (ICDE) – start-page: 586 year: 2015 end-page: 594 ident: bib0024 article-title: Diversity-induced multi-view subspace clustering publication-title: CVPR – year: 2020 ident: bib0032 article-title: Multiview concept learning via deep matrix factorization publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 7 start-page: 2399 year: 2006 end-page: 2434 ident: bib0019 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: bib0051 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: vol. 28 start-page: 1247 year: 2013 end-page: 1255 ident: bib0033 article-title: Deep canonical correlation analysis publication-title: ICML – start-page: 1597 year: 2020 end-page: 1607 ident: bib0035 article-title: A simple framework for contrastive learning of visual representations publication-title: International Conference on Machine Learning – start-page: 1204 year: 2019 end-page: 1209 ident: bib0022 article-title: Consistency meets inconsistency: a unified graph learning framework for multi-view clustering publication-title: ICDM – start-page: 6348 year: 2020 end-page: 6355 ident: bib0015 article-title: Multi-view multiple clusterings using deep matrix factorization publication-title: AAAI – start-page: 509 year: 2018 end-page: 520 ident: bib0003 article-title: Learning association relationship and accurate geometric structures for multi-type relational data publication-title: 2018 IEEE 34th International Conference on Data Engineering (ICDE) – volume: 217 start-page: 106807 year: 2021 ident: bib0050 article-title: Multi-view clustering via deep concept factorization publication-title: Knowl. Based Syst. – volume: 32 start-page: 45 year: 2010 end-page: 55 ident: bib0017 article-title: Convex and semi-nonnegative matrix factorizations publication-title: PAMI – volume: 39 start-page: 417 year: 2016 end-page: 429 ident: bib0013 article-title: A deep matrix factorization method for learning attribute representations publication-title: PAMI – volume: 48 start-page: 2620 year: 2018 end-page: 2632 ident: bib0028 article-title: Diverse non-negative matrix factorization for multiview data representation publication-title: IEEE Trans. Cybern. – volume: 88 start-page: 74 year: 2017 end-page: 89 ident: bib0029 article-title: Multi-view clustering via multi-manifold regularized non-negative matrix factorization publication-title: Neural Netw. – volume: 33 start-page: 1548 year: 2011 end-page: 1560 ident: bib0018 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: PAMI – start-page: 1083 year: 2015 end-page: 1092 ident: bib0034 article-title: On deep multi-view representation learning publication-title: ICML – volume: 8 start-page: 374 year: 2005 end-page: 384 ident: bib0046 article-title: Generative model-based document clustering: a comparative study publication-title: Knowl. Inf. Syst. – volume: 16 start-page: 57 year: 2007 end-page: 68 ident: bib0049 article-title: Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition publication-title: IEEE Trans. Image Process. – volume: vol. 39 year: 2008 ident: bib0047 article-title: Introduction to Information Retrieval – volume: 50 start-page: 3517 year: 2020 end-page: 3530 ident: bib0027 article-title: Dual shared-specific multiview subspace clustering publication-title: IEEE Trans. Cybern. – start-page: 131 year: 2018 end-page: 145 ident: bib0039 article-title: Scope and challenges of language modelling-an interrogative survey on context and embeddings publication-title: International Conference on Data Analytics and Management in Data Intensive Domains – year: 2004 ident: bib0045 article-title: Convex Optimization – start-page: 126 year: 2006 end-page: 135 ident: bib0044 article-title: Orthogonal nonnegative matrix t-factorizations for clustering publication-title: ACM SIGKDD – volume: 1 start-page: 83 year: 2018 end-page: 107 ident: bib0041 article-title: Multi-view clustering: a survey publication-title: Big Data Min. Anal. – start-page: 556 year: 2001 end-page: 562 ident: bib0005 article-title: Algorithms for non-negative matrix factorization publication-title: Advances in Neural Information Processing Systems 13 – start-page: 2750 year: 2015 end-page: 2756 ident: bib0009 article-title: Large-scale multi-view spectral clustering via bipartite graph publication-title: AAAI – start-page: 443 year: 2017 end-page: 452 ident: bib0031 article-title: A deep orthogonal non-negative matrix factorization method for learning attribute representations publication-title: International Conference on Neural Information Processing – start-page: 2921 year: 2017 end-page: 2927 ident: bib0014 article-title: Multi-view clustering via deep matrix factorization publication-title: AAAI – start-page: 849 year: 2001 end-page: 856 ident: bib0007 article-title: On spectral clustering: analysis and an algorithm publication-title: NIPS – volume: vol. 32 year: 2018 ident: bib0025 article-title: Consistent and specific multi-view subspace clustering publication-title: Proceedings of the AAAI Conference on Artificial Intelligence – start-page: 117 year: 2013 end-page: 124 ident: bib0008 article-title: Auto-encoder based data clustering publication-title: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications – year: 2018 ident: bib0023 article-title: Spectral clustering with distinction and consensus learning on multiple views data publication-title: PLoS ONE – volume: 6 start-page: 90 year: 2004 end-page: 105 ident: bib0006 article-title: Subspace clustering for high dimensional data: areview publication-title: SIGKDD Explor. Newsl. – volume: 115 start-page: 107890 year: 2021 ident: bib0026 article-title: A novel consensus learning approach to incomplete multi-view clustering publication-title: Pattern Recognit. – volume: 97 start-page: 107015 year: 2020 ident: bib0001 article-title: Auto-weighted multi-view clustering via deep matrix decomposition publication-title: Pattern Recognit. – start-page: 615 year: 2015 end-page: 626 ident: bib0048 article-title: Robust clustering of multi-type relational data via a heterogeneous manifold ensemble publication-title: 2015 IEEE 31st International Conference on Data Engineering – volume: 194 start-page: 105582 year: 2020 ident: bib0004 article-title: Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints publication-title: Knowl. Based Syst. – volume: 9 start-page: 2 year: 2021 ident: bib0040 article-title: A survey on contrastive self-supervised learning publication-title: Technologies – start-page: 1692 year: 2014 end-page: 1700 ident: bib0012 article-title: A deep semi-NMF model for learning hidden representations publication-title: ICML – start-page: 9729 year: 2020 end-page: 9738 ident: bib0036 article-title: Momentum contrast for unsupervised visual representation learning publication-title: The IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 22 start-page: 888 year: 2000 end-page: 905 ident: bib0020 article-title: Normalized cuts and image segmentation publication-title: PAMI – start-page: 6995 year: 2021 end-page: 7004 ident: bib0038 article-title: Multimodal contrastive training for visual representation learning publication-title: The IEEE/CVF Conference on Computer Vision and Pattern Recognition – start-page: 12475 year: 2021 end-page: 12486 ident: bib0037 article-title: Audio-visual instance discrimination with cross-modal agreement publication-title: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) – start-page: 252 year: 2013 ident: 10.1016/j.patcog.2022.108815_bib0010 article-title: Multi-view clustering via joint nonnegative matrix factorization – start-page: 117 year: 2013 ident: 10.1016/j.patcog.2022.108815_bib0008 article-title: Auto-encoder based data clustering – year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0032 article-title: Multiview concept learning via deep matrix factorization publication-title: IEEE Trans. Neural Netw. Learn. Syst. – start-page: 865 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0002 article-title: A novel approach to learning consensus and complementary information for multi-view data clustering – start-page: 1597 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0035 article-title: A simple framework for contrastive learning of visual representations – volume: 401 start-page: 788 issue: 6755 year: 1999 ident: 10.1016/j.patcog.2022.108815_bib0011 article-title: Learning the parts of objects by non-negative matrix factorization publication-title: Nature doi: 10.1038/44565 – volume: 50 start-page: 3517 issue: 8 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0027 article-title: Dual shared-specific multiview subspace clustering publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2918495 – volume: 32 start-page: 45 issue: 1 year: 2010 ident: 10.1016/j.patcog.2022.108815_bib0017 article-title: Convex and semi-nonnegative matrix factorizations publication-title: PAMI doi: 10.1109/TPAMI.2008.277 – start-page: 126 year: 2006 ident: 10.1016/j.patcog.2022.108815_bib0044 article-title: Orthogonal nonnegative matrix t-factorizations for clustering – volume: 48 start-page: 2620 issue: 9 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0028 article-title: Diverse non-negative matrix factorization for multiview data representation publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2017.2747400 – start-page: 9729 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0036 article-title: Momentum contrast for unsupervised visual representation learning – volume: 194 start-page: 105582 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0004 article-title: Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2020.105582 – year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0023 article-title: Spectral clustering with distinction and consensus learning on multiple views data publication-title: PLoS ONE doi: 10.1371/journal.pone.0208494 – volume: 8 start-page: 374 issue: 3 year: 2005 ident: 10.1016/j.patcog.2022.108815_bib0046 article-title: Generative model-based document clustering: a comparative study publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-004-0194-1 – volume: 33 start-page: 1548 issue: 8 year: 2011 ident: 10.1016/j.patcog.2022.108815_bib0018 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: PAMI doi: 10.1109/TPAMI.2010.231 – start-page: 509 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0003 article-title: Learning association relationship and accurate geometric structures for multi-type relational data – volume: 97 start-page: 107015 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0001 article-title: Auto-weighted multi-view clustering via deep matrix decomposition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107015 – start-page: 285 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0030 article-title: A novel technique of using coupled matrix and greedy coordinate descent for multi-view data representation – start-page: 6995 year: 2021 ident: 10.1016/j.patcog.2022.108815_bib0038 article-title: Multimodal contrastive training for visual representation learning – volume: 9 start-page: 2579 issue: 86 year: 2008 ident: 10.1016/j.patcog.2022.108815_bib0051 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 39 start-page: 417 issue: 3 year: 2016 ident: 10.1016/j.patcog.2022.108815_bib0013 article-title: A deep matrix factorization method for learning attribute representations publication-title: PAMI doi: 10.1109/TPAMI.2016.2554555 – start-page: 615 year: 2015 ident: 10.1016/j.patcog.2022.108815_bib0048 article-title: Robust clustering of multi-type relational data via a heterogeneous manifold ensemble – volume: 115 start-page: 107890 year: 2021 ident: 10.1016/j.patcog.2022.108815_bib0026 article-title: A novel consensus learning approach to incomplete multi-view clustering publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2021.107890 – volume: 217 start-page: 106807 year: 2021 ident: 10.1016/j.patcog.2022.108815_bib0050 article-title: Multi-view clustering via deep concept factorization publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2021.106807 – volume: vol. 28 start-page: 1247 year: 2013 ident: 10.1016/j.patcog.2022.108815_bib0033 article-title: Deep canonical correlation analysis – start-page: 1083 year: 2015 ident: 10.1016/j.patcog.2022.108815_bib0034 article-title: On deep multi-view representation learning – volume: 1 start-page: 83 issue: 2 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0041 article-title: Multi-view clustering: a survey publication-title: Big Data Min. Anal. doi: 10.26599/BDMA.2018.9020003 – volume: vol. 39 year: 2008 ident: 10.1016/j.patcog.2022.108815_bib0047 – start-page: 12475 year: 2021 ident: 10.1016/j.patcog.2022.108815_bib0037 article-title: Audio-visual instance discrimination with cross-modal agreement – start-page: 6348 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0015 article-title: Multi-view multiple clusterings using deep matrix factorization – volume: 9 start-page: 2 issue: 1 year: 2021 ident: 10.1016/j.patcog.2022.108815_bib0040 article-title: A survey on contrastive self-supervised learning publication-title: Technologies doi: 10.3390/technologies9010002 – volume: 22 start-page: 888 issue: 8 year: 2000 ident: 10.1016/j.patcog.2022.108815_bib0020 article-title: Normalized cuts and image segmentation publication-title: PAMI doi: 10.1109/34.868688 – start-page: 586 year: 2015 ident: 10.1016/j.patcog.2022.108815_bib0024 article-title: Diversity-induced multi-view subspace clustering – volume: 88 start-page: 74 year: 2017 ident: 10.1016/j.patcog.2022.108815_bib0029 article-title: Multi-view clustering via multi-manifold regularized non-negative matrix factorization publication-title: Neural Netw. doi: 10.1016/j.neunet.2017.02.003 – volume: 390 start-page: 108 year: 2020 ident: 10.1016/j.patcog.2022.108815_bib0042 article-title: Deep graph regularized non-negative matrix factorization for multi-view clustering publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.12.054 – volume: 16 start-page: 57 issue: 1 year: 2007 ident: 10.1016/j.patcog.2022.108815_bib0049 article-title: Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2006.884956 – volume: vol. 32 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0025 article-title: Consistent and specific multi-view subspace clustering – start-page: 1204 year: 2019 ident: 10.1016/j.patcog.2022.108815_bib0022 article-title: Consistency meets inconsistency: a unified graph learning framework for multi-view clustering – year: 2004 ident: 10.1016/j.patcog.2022.108815_bib0045 – start-page: 849 year: 2001 ident: 10.1016/j.patcog.2022.108815_bib0007 article-title: On spectral clustering: analysis and an algorithm – volume: vol. 24 start-page: 1413 year: 2011 ident: 10.1016/j.patcog.2022.108815_bib0021 article-title: Co-regularized multi-view spectral clustering – volume: 7 start-page: 2399 year: 2006 ident: 10.1016/j.patcog.2022.108815_bib0019 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – volume: 6 start-page: 90 issue: 1 year: 2004 ident: 10.1016/j.patcog.2022.108815_bib0006 article-title: Subspace clustering for high dimensional data: areview publication-title: SIGKDD Explor. Newsl. doi: 10.1145/1007730.1007731 – start-page: 443 year: 2017 ident: 10.1016/j.patcog.2022.108815_bib0031 article-title: A deep orthogonal non-negative matrix factorization method for learning attribute representations – start-page: 567 year: 2019 ident: 10.1016/j.patcog.2022.108815_bib0016 article-title: Self-weighted multi-view clustering with deep matrix factorization – start-page: 556 year: 2001 ident: 10.1016/j.patcog.2022.108815_bib0005 article-title: Algorithms for non-negative matrix factorization – start-page: 1692 year: 2014 ident: 10.1016/j.patcog.2022.108815_bib0012 article-title: A deep semi-NMF model for learning hidden representations – start-page: 131 year: 2018 ident: 10.1016/j.patcog.2022.108815_bib0039 article-title: Scope and challenges of language modelling-an interrogative survey on context and embeddings – volume: 7 issue: 11 year: 2006 ident: 10.1016/j.patcog.2022.108815_bib0043 article-title: Learning parts-based representations of data publication-title: J. Mach. Learn. Res. – start-page: 2921 year: 2017 ident: 10.1016/j.patcog.2022.108815_bib0014 article-title: Multi-view clustering via deep matrix factorization – start-page: 2750 year: 2015 ident: 10.1016/j.patcog.2022.108815_bib0009 article-title: Large-scale multi-view spectral clustering via bipartite graph |
| SSID | ssj0017142 |
| Score | 2.6255555 |
| Snippet | •An orthogonal deep non-negative matrix factorization (Deep-NMF) framework that aims to learn the non-linear parts-based representation for multi-view data is... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108815 |
| SubjectTerms | Deep Matrix Factorization (DMF) Deep Non-negative Matrix Factorization (Deep-NMF) Manifold learning Multi-view data/clustering Non-negative Matrix Factorization (NMF) |
| Title | Multi-layer manifold learning for deep non-negative matrix factorization-based multi-view clustering |
| URI | https://dx.doi.org/10.1016/j.patcog.2022.108815 |
| Volume | 131 |
| WOSCitedRecordID | wos000866467500004&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-5142 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017142 issn: 0031-3203 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLfKxoEL41MMBvKB2-Qqjh3cHCu0acCYdiiot8hfXbuFrOqaqvwb_MU8x3bSsWmwAxerihzH6vvl-f1e3gdC7zMjJOjFCckHJidcporkmjOXscyVpYyrptfh92NxcjIYj_PTXu9XzIVZlaKqBut1Pv-vooZrIGyXOnsPcbeLwgX4DUKHEcQO4z8JvkmpJaUEW9rFps4ml6WJzSF80KSxdr4PtJ9U9szX_f7hCvWvQ_OdkJlJ3AFnfMQhafJbdFm7sgrxsAsm7WlTodNlxYRQpO7D_nEdAn6_TGXV-Z3lT3kRk_pnnSsV7PgauLvbs0fpaDpb1JVc1RdyIX2DFj_zaurDwr-a_aEKkY3BcwGkl7aei6CNGSUsTdg1bRwOBa9PKShBn-55Q9V7r8N5fw5H1uVZ3z2g302_Xln7jxOvjUOMIW7nhV-lcKsUfpUHaDsVWQ6acnv46WD8uf02JSj3NejD7mNCZhM1eHM3txs8G0bM6Al6HNgHHnrUPEU9Wz1DO7GzBw6K_jkyGyDCEUQ4gggDiLADEd4EEfYgwreACHcgwh2IXqBvhwejj0ck9OMgGojlEt5f4divEpkagBk4yUD3Zzy3QPkVp8IwasQHzWCwqdRUSjB1GfBxo1MupNDsJdqCfdlXCJuUKg3MRCkg9MCAlU1kktgkh-Ul53oXsfinFToUq3c9U8riLpHtItLeNffFWv4yX0R5FMHg9IZkASC7887X93zSG_SoewP20NZyUdu36KFeLWdXi3cBYb8BuRGnQA |
| linkProvider | Elsevier |
| 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=Multi-layer+manifold+learning+for+deep+non-negative+matrix+factorization-based+multi-view+clustering&rft.jtitle=Pattern+recognition&rft.au=Luong%2C+Khanh&rft.au=Nayak%2C+Richi&rft.au=Balasubramaniam%2C+Thirunavukarasu&rft.au=Bashar%2C+Md+Abul&rft.date=2022-11-01&rft.issn=0031-3203&rft.volume=131&rft.spage=108815&rft_id=info:doi/10.1016%2Fj.patcog.2022.108815&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_patcog_2022_108815 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0031-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0031-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0031-3203&client=summon |