Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2024

08.10.2023 | Original Article

A multiple kinds of information extraction method for multi-view low-rank subspace clustering

verfasst von: Jianxi Zhao, Xiaonan Wang, Qingrong Zou, Fangyuan Kang, Fan Wang, Jingfu Peng

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Recently, multi-view subspace clustering has attracted intensive attentions due to the remarkable clustering performance by extracting abundant complementary information from multi-view data, making its clustering performance much better than that of single view data. However, at present, multi-view subspace clustering methods develop either a shared consistency representation that models the common properties from all views, or a series of specificity representations each of which mines the intrinsic difference in each view, or global spatial structure of all features, or local geometric structure of multiple features. More seriously, only one kind of information is extracted and utilized in some research work. In this paper, to cope with the issue, we present a multiple kinds of information extraction method (MKIE) for multi-view subspace clustering, which combines the consistency and specificity regularizations with the graph regularizations. We construct some graph structures for the shared consistency representation and all the specificity representations, which model the local geometric structures of multiple features. The model of MKIE makes full use of four kinds of valid information: global spatial structure information, local geometric structure information, consistent feature information and specific feature information. In addition, we design an effective optimization algorithm based on Alternating Direction Method of Multipliers. Extensive experiments performed on four benchmark multi-view datasets validate the effectiveness of MKIE which is compared with ten state-of-the-art multi-view clustering methods.
Vorheriger Artikel A hybrid similarity measure-based clustering approach for mixed attribute data
Nächster Artikel Underwater image enhancement based on multiscale fusion generative adversarial network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Fußnoten
Literatur
1.
Mirkin B (2005) Clustering for data mining: a data recovery approach. Chapman & Hall/CRC, Los Angeles
2.
Fu L, Lin P, Vasilakos AV et al (2020) An overview of recent multi-view clustering. Neurocomputing 402:148–161
3.
Xie Y, Lin B, Qu Y et al (2020) Joint deep multi-view learning for image clustering. IEEE Trans Knowl Data Eng 33(11):3594–3606
4.
Zhao J (2022) A novel low-rank matrix approximation algorithm for face denoising and background/foreground separation. Comput Appl Math 41(4):1–38MathSciNet
5.
Bertsimas D, Orfanoudaki A, Wiberg H (2021) Interpretable clustering: an optimization approach. Mach Learn 110(1):89–138MathSciNet
6.
Ghadiri S, Mazlumi K (2020) Adaptive protection scheme for microgrids based on SOM clustering technique. Appl Soft Comput 88:106062
7.
Kouhi A, Seyedarabi H, Aghagolzadeh A (2020) Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation. Expert Syst Appl 146:113159
8.
Lu C, Feng J, Lin Z et al (2018) Subspace clustering by block diagonal representation. IEEE Trans Pattern Anal Mach Intell 41(2):487–501PubMed
9.
Zhao J, Zhao L (2020) Low-rank and sparse matrices fitting algorithm for low-rank representation. Comput Math Appl 79(2):407–425MathSciNet
10.
Baker Y, Tang T, Allen G (2020) Feature selection for data integration with mixed multiview data. Annals of Applied Statistics 14(4):1676–1698MathSciNet
11.
Liu X, Li M, Tang C et al (2020) Efficient and effective regularized incomplete multi-view clustering. IEEE Trans Pattern Anal Mach Intell 43(8):2634–2646
12.
Li R, Zhang C, Fu H, et al (2019) Reciprocal multi-layer subspace learning for multi-view clustering. Proceedings of the IEEE/CVF International Conference on Computer Vision 8172–8180
13.
Yang Z, Xu Q, Zhang W et al (2019) Split multiplicative multi-view subspace clustering. IEEE Trans Image Process 28(10):5147–5160ADSMathSciNet
14.
Zhao J, Feng Q, Zhao L (2019) Alternating direction and Taylor expansion minimization algorithms for unconstrained nuclear norm optimization. Numerical Algorithms 82(1):371–396MathSciNet
15.
Zhang X, Sun H, Liu Z et al (2019) Robust low-rank kernel multi-view subspace clustering based on the Schatten p-norm and correntropy. Inf Sci 477:430–447
16.
Peng X, Huang Z, Lv J, et al (2019) COMIC: multi-view clustering without parameter selection. International Conference on Machine Learning 5092–5101
17.
Huang S, Kang Z, Tsang I et al (2019) Auto-weighted multi-view clustering via kernelized graph learning. Pattern Recogn 88:174–184ADS
18.
Yu H, Wang X, Wang G et al (2020) An active three-way clustering method via low-rank matrices for multi-view data. Inf Sci 507:823–839
19.
Yin M, Gao J, Xie S et al (2018) Multiview subspace clustering via tensorial t-product representation. IEEE Transactions on Neural Networks and Learning Systems 30(3):851–864MathSciNetPubMed
20.
Chen Y, Xiao X, Zhou Y (2020) Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix. Pattern Recogn 106:107441
21.
Gao Q, Xia W, Wan Z et al (2020) Tensor-SVD based graph learning for multi-view subspace clustering. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):3930–3937
22.
Zhu X, Zhang S, He W et al (2018) One-step multi-view spectral clustering. IEEE Trans Knowl Data Eng 31(10):2022–2034
23.
Wang Y, Wu L, Lin X et al (2018) Multiview spectral clustering via structured low-rank matrix factorization. IEEE Transactions on Neural Networks and Learning Systems 29(10):4833–4843PubMed
24.
Wang Y, Wu L (2018) Beyond low-rank representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering. Neural Netw 103:1–8PubMed
25.
Sharma K, Seal A (2021) Multi-view spectral clustering for uncertain objects. Inf Sci 547:723–745MathSciNet
26.
Wang H, Yang Y, Liu B et al (2019) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163:1009–1019
27.
Zhang C, Fu H, Hu Q et al (2018) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86–99PubMed
28.
Brbić M, Kopriva I (2018) Multi-view low-rank sparse subspace clustering. Pattern Recogn 73:247–258ADS
29.
Luo S, Zhang C, Zhang W et al (2018) Consistent and specific multi-view subspace clustering. Proceedings of the AAAI Conference on Artificial Intelligence 32(1):3110–3126
30.
Zhang G, Zhou Y, He X et al (2020) One-step kernel multi-view subspace clustering. Knowl-Based Syst 189:105126
31.
Zheng Q, Zhu J, Li Z et al (2020) Feature concatenation multi-view subspace clustering. Neurocomputing 379:89–102
32.
Wang H, Yang Y, Liu B (2019) GMC: Graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116–1129
33.
Zhao L, Chen Z, Yang Y et al (2018) Incomplete multi-view clustering via deep semantic mapping. Neurocomputing 275:1053–1062
34.
Jing P, Su Y, Li Z et al (2021) Learning robust affinity graph representation for multi-view clustering. Inf Sci 544:155–167MathSciNet
35.
Wu J, Xie X, Nie L et al (2020) Unified graph and low-rank tensor learning for multi-view clustering. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):6388–6395
36.
Tang C, Liu X, Zhu X et al (2020) CGD: Multi-view clustering via cross-view graph diffusion. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):5924–5931
37.
Li L, He H (2020) Bipartite graph based multi-view clustering. IEEE Transactions on Knowledge and Data Engineering 34(7):3111–3125
38.
Xia W, Wang Q, Gao Q et al (2021) Self-supervised graph convolutional network for multi-view clustering. IEEE Transact Multimedia 24:3182–3192
39.
Li J, Zhou G, Qiu Y et al (2020) Deep graph regularized non-negative matrix factorization for multi-view clustering. Neurocomputing 390:108–116
40.
Li Z, Tang C, Liu X et al (2021) Consensus graph learning for multi-view clustering. IEEE Transact Multimedia 24:2461–2472
41.
Wang Y, Chang D, Fu Z et al (2021) Consistent multiple graph embedding for multi-view clustering. IEEE Transact Multimedia 25:1008–1018
42.
Huang S, Tsang I, Xu Z et al (2021) Measuring diversity in graph learning: a unified framework for structured multi-view clustering. IEEE Transact Knowledge Data Eng 34(12):5869–5883
43.
Yang B, Zhang X, Chen B et al (2022) Efficient correntropy-based multi-view clustering with anchor graph embedding. Neural Netw 146:290–302PubMed
44.
Shi S, Nie F, Wang R et al (2021) Multi-view clustering via nonnegative and orthogonal graph reconstruction. IEEE Transactions on Neural Networks and Learning Systems 34(1):201–214
45.
Shi S, Nie F, Wang R et al (2021) Fast multi-view clustering via prototype graph. IEEE Transactions on Knowledge and Data Engineering 35(1):443–455
46.
Mei Y, Ren Z, Wu B et al (2022) Robust graph-based multi-view clustering in latent embedding space. Int J Mach Learn Cybern 13(2):497–508
47.
Wang R, Li L, Tao X et al (2022) Contrastive and attentive graph learning for multi-view clustering. Inf Process Manage 59(4):102967
48.
Li M, Liang W, Liu X (2021) Multi-view clustering with learned bipartite graph. IEEE Access 9:87952–87961
49.
Shu X, Zhang X, Wang Q (2022) Self-weighted graph learning for multi-view clustering. Neurocomputing 501:188-196
50.
Gu Z, Feng S (2022) Individuality meets commonality: a unified graph learning framework for multi-view clustering. ACM Trans Knowl Disc Data 17(1):1–21
51.
Liu L, Chen P, Luo G et al (2022) Scalable multi-view clustering with graph filtering. Neural Comput Appl 34(19):16213–16221
52.
Lu X, Feng S (2022) Structure diversity-induced anchor graph fusion for multi-view clustering. ACM Trans Knowl Disc Data 17(2):1–18
53.
Lu H, Gao Q, Zhang X et al (2022) A multi-view clustering framework via integrating k-means and graph-cut. Neurocomputing 501:609–617
54.
Jiang T, Gao Q, Gao X (2021) Multiple graph learning for scalable multi-view clustering. arXiv preprint arXiv: 2106.15382
55.
Wang C, Geng L, Zhang J et al (2022) Multi-view clustering via robust consistent graph learning. Digit Signal Process 128:103607
56.
Yang B, Zhang X, Lin Z et al (2022) Efficient and robust multi-view clustering with anchor graph regularization. IEEE Transactions on Circuits and Systems for Video Technology 32(9):6200–6213
57.
Jiang G, Peng J, Wang H et al (2022) Tensorial multi-view clustering via low-rank constrained high-order graph learning. IEEE Transactions on Circuits and Systems for Video Technology 32(8):5307–5318
58.
Gao Q, Xia W, Gao X et al (2021) Effective and efficient graph learning for multi-view clustering. arXiv preprint arXiv: 2108.06734
59.
Huang S, Kang Z, Xu Z (2020) Auto-weighted multi-view clustering via deep matrix decomposition. Pattern Recogn 97:107015
60.
Xu J, Ren Y, Li G et al (2021) Deep embedded multi-view clustering with collaborative training. Inf Sci 573:279–290MathSciNet
61.
Wang Q, Cheng J, Gao Q et al (2020) Deep multi-view subspace clustering with unified and discriminative learning. IEEE Trans Multimedia 23:3483–3493
62.
Liu J, Cao F, Gao X et al (2020) A cluster-weighted kernel K-means method for multi-view clustering. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):4860–4867
63.
El Hajjar S, Dornaika F, Abdallah F et al (2022) Consensus graph and spectral representation for one-step multi-view kernel based clustering. Knowl-Based Syst 241:108250
64.
Zhu W, Lu J, Zhou J (2019) Structured general and specific multi-view subspace clustering. Pattern Recogn 93:392–403ADS
65.
Li Z, Tang C, Chen J et al (2019) Diversity and consistency learning guided spectral embedding for multi-view clustering. Neurocomputing 370:128–139
66.
Mi Y, Ren Z, Mukherjee M et al (2021) Diversity and consistency embedding learning for multi-view subspace clustering. Appl Intell 51(10):6771–6784
67.
Si X, Yin Q, Zhao X et al (2022) Consistent and diverse multi-view subspace clustering with structure constraint. Pattern Recogn 121:108196
68.
Wang S, Liu X, Zhu X et al (2021) Fast parameter-free multi-view subspace clustering with consensus anchor guidance. IEEE Trans Image Process 31:556–568ADSPubMed
69.
Kang Z, Zhou W, Zhao Z et al (2020) Large-scale multi-view subspace clustering in linear time. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):4412–4419
70.
Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. Proceedings of the 28th International Conference on Machine Learning 393–400
71.
Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. Adv Neural Inform Proces Syst 24:1413–1421
72.
Zhang C, Fu H, Liu S, et al (2015) Low-rank tensor constrained multiview subspace clustering. Proceedings of the IEEE International Conference on Computer Vision 1582–1590
73.
Cao X, Zhang C, Fu H, et al (2015) Diversity-induced multi-view subspace clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 586–594
74.
Ng A, Jordan M, Weiss Y (2001) On spectral clustering: Analysis and an algorithm. Adv Neural Inform Proces Syst 14:849–856
75.
Hiriart-Urruty J, Lemaréchal C (2013) Convex analysis and minimization algorithms I: Fundamentals. Springer Science & Business Media, New York
76.
Yang J, Yin W, Zhang Y et al (2009) A fast algorithm for edge-preserving variational multichannel image restoration. SIAM J Imag Sci 2(2):569–592MathSciNet
77.
Bartels R, Stewart G (1972) Solution of the matrix equation AX + XB = C [F4]. Commun ACM 15(9):820–826
78.
Xia R, Pan Y, Du L, et al (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of AAAI Conference on Artificial Intelligence 2149–2155
79.
Geusebroek J, Burghouts G, Smeulders A (2005) The Amsterdam library of object images. Int J Comput Vision 61(1):103–112
80.
Brbić M, Piškorec M, Vidulin V, et al (2016) The landscape of microbial phenotypic traits and associated genes. Nucleic Acids Res gkw964
81.
Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Conference on Computer Vision and Pattern Recognition Workshop 2004:178–178
82.
Zhan K, Zhang C, Guan J et al (2017) Graph learning for multiview clustering. IEEE Transact Cybern 48(10):2887–2895
83.
Wang X, Lei Z, Guo X et al (2019) Multi-view subspace clustering with intactness-aware similarity. Pattern Recogn 88:50–63ADS
84.
Zhang Z, Liu L, Shen F et al (2019) Binary multi-view clustering. IEEE Trans Pattern Anal Mach Intell 41(7):1774–1782PubMed
85.
Liu J, Liu X, Yang Y, et al (2021) One-pass multi-view clustering for large-scale data. Proceedings of the IEEE/CVF International Conference on Computer Vision 12344–12353
86.
Tang C, Li Z, Wang J, et al (2022) Unified one-step multi-view spectral clustering. IEEE Transact Knowledge Data Eng 35(6):6449-6460
87.
Huang D, Wang C, Lai J (2017) Locally weighted ensemble clustering. IEEE Transact Cybern 48(5):1460–1473
88.
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNet
89.
Zhao J, Kang F, Zou Q et al (2023) Multi-view clustering with orthogonal mapping and binary graph. Expert Syst Appl 213:118911
90.
Zhao J, Wang X, Zou Q et al (2023) On improvability of hash clustering data from different sources by bipartite graph. Pattern Anal Appl 26(2):555–570
Metadaten
Titel
A multiple kinds of information extraction method for multi-view low-rank subspace clustering
verfasst von
Jianxi Zhao
Xiaonan Wang
Qingrong Zou
Fangyuan Kang
Fan Wang
Jingfu Peng
Publikationsdatum
08.10.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01969-5

Weitere Artikel der Ausgabe 4/2024

International Journal of Machine Learning and Cybernetics 4/2024 Zur Ausgabe

Original Article

Entropy based swarm intelligent searching for scheduling deadline constrained workflows in hybrid cloud

Original Article

Depth color correlation-guided dark channel prior for underwater image enhancement

Original Article

An interpretable neural network for robustly determining the location and number of cluster centers

Original Article

Enhancing framelet GCNs with generalized p-Laplacian regularization

Original Article

Efficient low-light image enhancement with model parameters scaled down to 0.02M

Original Article

Assessment of patients with Parkinson’s disease based on federated learning

Neuer Inhalt

    Bildnachweise