Skip to main content
SpringerLink
Log in

Essential multi-view graph learning for clustering

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Multi-view clustering utilizes information from diverse views to improve the performance of clustering. For most existing multi-view spectral clustering methods, information of different views is integrated by pursuing a consensus similarity matrix for clustering. However, view-specific structures, which contain the complementary information of multi-view data, may be lost during the clustering process. Actually, in multi-view spectral clustering, similarity matrices of multiple views would have the same clustering structures or properties rather than be numerically uniform. To overcome the aforementioned problem, a novel essential multi-view graph learning (EMGL) method for clustering is proposed in this paper. Different from most existing multi-view spectral clustering, an orthogonal matrix factorization is imposed on multi-view similarity matrices for making them have the same nuclear norm, which indicates the same clustering structures of different views. Furthermore, we also propose an alternating direction method of multipliers (ADMM) based optimization algorithm to address the objective function of our method. Extensive experiments on several datasets demonstrate the superior performance of our proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://research.microsoft.com/en-us/projects/objectclassrecognition/.

  2. http://mlg.ucd.ie/datasets/bbc.html.

  3. http://www.vision.caltech.edu/ImageDatasets/Caltech101/.

References

  • Brbić M, Piškorec M, Vidulin V, Kriško A, Šmuc T, Supek F (2016) The landscape of microbial phenotypic traits and associated genes. Nucleic Acids Res 21:10074–10090

    Google Scholar 

  • Cai JF, Candès EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982

    Article  MathSciNet  Google Scholar 

  • Chen Z, Lu H, Tian S, Qiu J, Kamiya T, Serikawa S, Xu L (2020) Construction of a hierarchical feature enhancement network and its application in fault recognition. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2020.3021688

    Article  Google Scholar 

  • Cortes C, Mohri M, Rostamizadeh A (2009) Learning non-linear combinations of kernels. Adv Neural Inf Process Syst 22:396–404

    Google Scholar 

  • Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781

    Article  Google Scholar 

  • 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. In: 2004 conference on computer vision and pattern recognition workshop. IEEE, pp 178–178

  • Hu Y, Zhang D, Ye J, Li X, He X (2012) Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Trans Pattern Anal Mach Intell 35(9):2117–2130

    Article  Google Scholar 

  • Huang L, Lu J, Tan YP (2014) Co-learned multi-view spectral clustering for face recognition based on image sets. IEEE Signal Process Lett 21(7):875–879

    Article  Google Scholar 

  • Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning, pp 393–400

  • Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. In: Advances in neural information processing systems, pp 1413–1421

  • Lan R, Sun L, Liu Z, Lu H, Pang C, Luo X (2021) Madnet: a fast and lightweight network for single-image super resolution. IEEE Trans Cybern 51(3):1443–1453

    Article  Google Scholar 

  • Lin Z, Liu R, Su Z (2011) Linearized alternating direction method with adaptive penalty for low-rank representation. In: Advances in neural information processing systems, pp 612–620

  • Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2012) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  • Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J 5(4):2315–2322

    Article  Google Scholar 

  • Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mob Netw Appl 23(2):368–375

    Article  Google Scholar 

  • Lu H, Zhang M, Xu X, Li Y, Shen HT (2021a) Deep fuzzy hashing network for efficient image retrieval. IEEE Trans Fuzzy Syst 29(1):166–176

    Article  Google Scholar 

  • Lu H, Zhang Y, Li Y, Jiang C, Abbas H (2020b) User-oriented virtual mobile network resource management for vehicle communications. IEEE trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2991766

    Article  Google Scholar 

  • Lu W, Zhang Y, Wang S, Huang H, Liu Q, Luo S (2020c) Concept representation by learning explicit and implicit concept couplings. IEEE Intell Syst. https://doi.org/10.1109/MIS.2020.3021188

    Article  Google Scholar 

  • Luo S, Zhang C, Zhang W, Cao X (2018) Consistent and specific multi-view subspace clustering. In: Thirty-second AAAI conference on artificial intelligence

  • Ma S, Liu Y, Zheng Q, Li Y, Cui Z (2020) Multiview spectral clustering via complementary information. Concurr Comput-Pract Exp. https://doi.org/10.1002/cpe.5701

    Article  Google Scholar 

  • Ng A, Jordan M, Weiss Y (2001) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst 14:849–856

    Google Scholar 

  • Nie F, Li J, Li X, et al (2016) Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In: International joint conference on artificial intelligence, pp 1881–1887

  • Oh TH, Tai YW, Bazin JC, Kim H, Kweon IS (2015) Partial sum minimization of singular values in robust pca: algorithm and applications. IEEE Trans Pattern Anal Mach Intell 38(4):744–758

    Article  Google Scholar 

  • Ou W, Yu S, Li G, Lu J, Zhang K, Xie G (2016) Multi-view non-negative matrix factorization by patch alignment framework with view consistency. Neurocomputing 204:116–124

    Article  Google Scholar 

  • Schönemann PH (1966) A generalized solution of the orthogonal procrustes problem. Psychometrika 31(1):1–10

    Article  MathSciNet  Google Scholar 

  • Schütze H, Manning CD, Raghavan P (2008) Introduction to information retrieval, vol 39. Cambridge University Press Cambridge

  • Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50

    Article  Google Scholar 

  • Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

    Article  MathSciNet  Google Scholar 

  • Wang H, Yang Y, Liu B (2019) Gmc: graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116–1129

    Article  Google Scholar 

  • Wang P, Wang D, Zhang X, Li X, Peng T, Lu H, Tian X (2020) Numerical and experimental study on the maneuverability of an active propeller control based wave glider. Appl Ocean Res 104:102369

    Article  Google Scholar 

  • Xia R, Pan Y, Du L, Yin J (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, pp 2149–2155

  • Xu C, Tao D, Xu C (2013) A survey on multi-view learning. arXiv:13045634

  • Xu X, Lu H, Song J, Yang Y, Shen HT, Li X (2019) Ternary adversarial networks with self-supervision for zero-shot cross-modal retrieval. IEEE Trans Cybern 50(6):2400–2413

    Article  Google Scholar 

  • Yin M, Gao J, Lin Z, Shi Q, Guo Y (2015a) Dual graph regularized latent low-rank representation for subspace clustering. IEEE Trans Image Process 24(12):4918–4933

    Article  MathSciNet  Google Scholar 

  • Yin Q, Wu S, He R, Wang L (2015b) Multi-view clustering via pairwise sparse subspace representation. Neurocomputing 156:12–21

    Article  Google Scholar 

  • Zhai L, Zhu J, Zheng Q, Pang S, Li Z, Wang J (2019) Multi-view spectral clustering via partial sum minimisation of singular values. Electron Lett 55(6):314–316

    Article  Google Scholar 

  • Zhang C, Fu H, Liu S, Liu G, Cao X (2015) Low-rank tensor constrained multiview subspace clustering. In: Proceedings of the IEEE international conference on computer vision, pp 1582–1590

  • Zhang C, Fu H, Hu Q, Cao X, Xie Y, Tao D, Xu D (2018) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86–99

    Article  Google Scholar 

  • Zhang L, Zhang D (2015) Visual understanding via multi-feature shared learning with global consistency. IEEE Trans Multimed 18(2):247–259

    Article  Google Scholar 

  • Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

  • Zhang Z, Xu Y, Shao L, Yang J (2017) Discriminative block-diagonal representation learning for image recognition. IEEE Trans Neural Netw Learn Syst 29(7):3111–3125

    Article  MathSciNet  Google Scholar 

  • Zhao J, Xie X, Xu X, Sun S (2017) Multi-view learning overview: Recent progress and new challenges. Inf Fusion 38:43–54

    Article  Google Scholar 

  • Zheng Q, Zhu J, Li Z, Pang S, Wang J, Li Y (2020) Feature concatenation multi-view subspace clustering. Neurocomputing 379:89–102

    Article  Google Scholar 

  • Zhou D, Burges CJ (2007) Spectral clustering and transductive learning with multiple views. In: Proceedings of the 24th international conference on machine learning, pp 1159–1166

Download references

Acknowledgements

This research was supported by the National Key R&D Program of China (Grant No: 2018AAA0102504).

Author information

Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Shuangxun Ma & Qinghai Zheng

  2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, 710049, Xi’an, China

    Yuehu Liu

Authors
  1. Shuangxun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qinghai Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuehu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuehu Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, S., Zheng, Q. & Liu, Y. Essential multi-view graph learning for clustering. J Ambient Intell Human Comput 13, 5225–5236 (2022). https://doi.org/10.1007/s12652-021-03002-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03002-5

Keywords

Search

Navigation