Skip to main content
SpringerLink
Log in

One-step multi-view spectral clustering by learning common and specific nonnegative embeddings

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Multi-view spectral clustering is a hot research area which has attracted increasing attention. Most existing multi-view spectral clustering methods utilize a two-step strategy. The first step obtains a common embedding by fusing spectral embeddings of different views, and the second step conducts hard clustering, such as K-means or spectral rotation, on the common embedding. Because the goal of the first step is not obtaining optimal clustering result, and the requirement to post-processing makes the final clustering result uncertain. In this paper, we propose a novel one-step multi-view spectral clustering method, in which the spectral embedding and nonnegative embedding are unified into one framework. Therefore, our method can avoid the uncertainty brought by post-processing and obtain optimal clustering result. Moreover, the nonnegative embedding is divided into two parts. The common nonnegative embedding indicates the shared cluster structure, and the specific nonnegative embedding indicates the exclusive cluster structure of each view. Hence, our method can well tackle with noises and outliers of different views. Furthermore, an alternating iterative algorithm is used to solve the joint optimization problem. Extensive experimental results on four real-world datasets have demonstrated the effectiveness of the 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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php

  2. https://github.com/sudalvxin/2019-PR-Sparse-Multi-view-clustering/tree/master/Data.

  3. https://github.com/yeqinglee/mvdata.

  4. http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html.

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

References

  1. Wang B, Feng K, Yang W, Zhu Z (2019) Study on multiple targets tracking algorithm based on multiple sensors. Cluster Comput 22(6):13283–13291

    Article  Google Scholar 

  2. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  3. Qian, M, Zhai, C (2014) Unsupervised feature selection for multi-view clustering on text-image web news data. In: Proceedings of the 23th ACM International Conference on Information and Knowledge Management, CIKM, pp. 1963–1966

  4. Fu L, Lin P, Vasilakos AV, Wang S (2020) An overview of recent multi-view clustering. Neurocomputing 402:148–161

    Article  Google Scholar 

  5. Cao X, Zhang C, Fu H, Liu S, Zhang H (2015) Diversity-induced multi-view subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 586–594

  6. Zhu W, Lu J, Zhou J (2019) Structured general and specific multi-view subspace clustering. Pattern Recogn 93:392–403

    Article  Google Scholar 

  7. Brbic M, Kopriva I (2018) Multi-view low rank sparse subspace clustering. Pattern Recog 73:247–258

    Article  Google Scholar 

  8. Zhang C, Fu H, Hu Q, Cao X, Xie Y, Tao D, Xu D (2020) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86–99

    Article  Google Scholar 

  9. Liu J, Wang C, Gao J, Han J (2013) Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 13th SIAM International Conference on Data Mining, SDM, pp. 252–260

  10. Zong L, Zhang X, Zhao L, Yu H, Zhao Q (2017) Multi-view clustering via multi-manifold regularized nonnegative matrix factorization. Neural Netw 88:74–89

    Article  Google Scholar 

  11. Mekthanavanh V, Li T, Meng H, Yang Y, Hu J (2019) Social web video clustering based on multi-view clustering via nonnegative matrix factorization. Int J Mach Learn Cybern 10(10):2779–2790

    Article  Google Scholar 

  12. Peng X, Huang Z, Lv J, Zhu H, Zhou JT (2019) Comic: multi-view clustering without parameter selection. In: Proceedings of the 36th International Conference on Machine Learning,ICML, Vol. 97, pp. 5092–5101

  13. Gao Q, Wan Z, Liang Y, Wang Q, Liu Y, Shao L (2020) Multi-view projected clustering with graph learning. Neural Netw 126:335–346

    Article  Google Scholar 

  14. Huang S, Xu Z, Tsang IW, Kang Z (2020) Auto-weighted multi-view co-clustering with bipartite graphs. Inform Sci 512:18–30

    Article  MathSciNet  Google Scholar 

  15. Kumar A (2011) H. D. III, A co-training approach for multi-view spectral clustering, in: Proceedings of the 28th International Conference on Machine Learning, ICML, pp. 393–400

  16. Kumar A, Rai P (2011) H. D. III, Co-regularized multi-view spectral clustering, in: Proceedings of the 25th Annual Conference on Neural Information Processing Systems, NIPS, pp. 1413–1421

  17. Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B 40(6):1438–1446

    Article  Google Scholar 

  18. Yin H, Li F, Zhang L, Zhang Z (2019) Multi-view clustering via spectral embedding fusion. Soft Comput 23(1):343–356

    Article  Google Scholar 

  19. Hu Z, Nie F, Chang W, Hao S, Wang R, Li X (2020) Multi-view spectral clustering via sparse graph learning. Neurocomputing 384:1–10

    Article  Google Scholar 

  20. Hu Z, Nie F, Wang R, Li X (2020) Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding. Informat Fus 55:251–259

    Article  Google Scholar 

  21. Luo Y, Wang X, Cao W (2020) A novel dataset-specific feature extractor for zero-shot learning. Neurocomputing 391:74–82

    Article  Google Scholar 

  22. Yin Q, Wu S, Wang L (2018) Multiview clustering via unified and view-specific embeddings learning. IEEE Trans Neural Netw Learn Syst 29(11):5541–5553

    Article  MathSciNet  Google Scholar 

  23. Li Z, Tang C, Chen J, Wan C, Yan W, Liu X (2019) Diversity and consistency learning guided spectral embedding for multi-view clustering. Neurocomputing 370:128–139

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  25. Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: Proceedings of the 15th Annual Conference on Neural Information Processing Systems, NIPS, pp. 849–856

  26. Chen X, Sun W, Wang B, Li Z, Wang X, Ye Y (2019) Spectral clustering of customer transaction data with a two-level subspace weighting method. IEEE Trans Syst Man Cybern 49(9):3230–3241

    Google Scholar 

  27. Han J, Xiong K, Nie F (2017) Orthogonal and nonnegative graph reconstruction for large scale clustering. In: Sierra C (ed) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, ijcai.org, pp. 1809–1815

  28. Nie F, Tian L, Li X (2018) Multiview clustering via adaptively weighted procrustes. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pp. 2022–2030

  29. Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  30. Ding CHQ, He X (2005) On the equivalence of nonnegative matrix factorization and spectral clustering. In: Kargupta H, Srivastava J, Kamath C, Goodman A (eds) Proceedings of the 2005 SIAM International Conference on Data Mining, SDM, pp. 606–610

  31. Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI, pp. 2750–2756

  32. Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. In: Advances in neural information processing systems 17. NIPS, pp. 1601–1608

  33. Hinton GE (2008) Visualizing high-dimensional data using t-sne. J Mach Learn Res 9(2):2579–2605

    MATH  Google Scholar 

  34. Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn 29(11):5304–5318

    Article  MathSciNet  Google Scholar 

  35. Xu C, Guan Z, Zhao W, Niu Y, Wang Q, Wang Z (2018) Deep multi-view concept learning. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, pp. 2898–2904

  36. Wang W, Arora R, Livescu K, Bilmes JA (2015) On deep multi-view representation learning. In: Proceedings of the 32nd International Conference on Machine Learning, ICML, Vol. 37, pp. 1083–1092

Download references

Funding

This work has been partially supported by grants from National Natural Science Foundation of China (No. 61772198, No. 61672364), Zhejiang Basic Public Welfare Research Project (No. LGN18F020002) and the Natural Science Foundation of Zhejiang Province (No. LR20F020002).

Author information

Authors and Affiliations

  1. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Hongwei Yin, Wenjun Hu & Jungang Lou

  2. Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, 313000, China

    Hongwei Yin, Wenjun Hu & Jungang Lou

  3. College of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Fanzhang Li

Authors
  1. Hongwei Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fanzhang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jungang Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Yin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Yin, H., Hu, W., Li, F. et al. One-step multi-view spectral clustering by learning common and specific nonnegative embeddings. Int. J. Mach. Learn. & Cyber. 12, 2121–2134 (2021). https://doi.org/10.1007/s13042-021-01297-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01297-6

Keywords

Search

Navigation