Skip to main content
SpringerLink
Log in

Multi-view clustering via spectral embedding fusion

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Multi-view learning, such as multi-view feature learning and multi-view clustering, has a wide range of applications in machine learning and pattern recognition. Most previous studies employ the multiple data information from various views to improve the performance of learning. The key problem is to integrate the symbiotic part of the different views or datasets. In practical clustering task, the symbiotic part includes two levels: global structure information and local structure information. However, traditional multi-view clustering methods usually ignore the energy of the local structure information. This paper proposes a novel multi-view clustering model to solve this problem, which simultaneously integrates the global structure information and local structure information of all the views. By integrating the fusion of global spectral embedding and the fusion of spectral manifold embedding from multi-view data, we construct an objective function to find the final fusional embedding and give an iteration method to solve it by using the \(L_{2,1}\) norm. Finally, the K-means clustering method is applied to the obtained final fusional embedding. Extensive experimental results on several real multi-view data sets demonstrate the superior performance of our model.

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

Access this article

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://archive.ics.uci.edu/ml/datasets/Multiple+Features.

  2. http://www.vision.caltech.edu/Image_Datasets/Caltech101/.

  3. http://lms.comp.nus.edu.sg/research/NUS-WIDE.html.

  4. https://archive.ics.uci.edu/ml/datasets.html.

References

  • Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems 14 [Neural information processing systems: natural and synthetic, NIPS 2001, December 3–8, 2001, Vancouver, British Columbia, Canada], pp 585–591

  • Cai X, Nie F, Huang H, Kamangar F (2011) Heterogeneous image feature integration via multi-modal spectral clustering. In: The 24th IEEE conference on computer vision and pattern recognition, CVPR 2011, Colorado Springs, CO, USA, 20–25 June 2011, pp 1977–1984. https://doi.org/10.1109/CVPR.2011.5995740

  • Chang X, Nie F, Ma Z, Yang Y, Zhou X (2015) A convex formulation for spectral shrunk clustering. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, January 25–30, 2015, Austin, Texas, USA, pp 2532–2538

  • Dhillon PS, Foster DP, Ungar LH (2011) Multi-view learning of word embeddings via CCA. In: Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011. Proceedings of a meeting held 12–14 December 2011, Granada, Spain, pp 199–207

  • Gao H, Nie F, Li X, Huang H (2015) Multi-view subspace clustering. In: 2015 IEEE international conference on computer vision, ICCV 2015, Santiago. https://doi.org/10.1109/ICCV.2015.482

  • Kumar A, Daume III H (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning, ICML 2011, Bellevue, Washington, USA, June 28–July 2, 2011, pp 393–400

  • Kumar A, Rai P, Daume III H (2011) Co-regularized multi-view spectral clustering. In: Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011. Proceedings of a meeting held 12–14 December 2011, Granada, Spain, pp 1413–1421

  • Li Y, Nie F, Huang H, Huang J (2015) Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the twenty-ninth AAAI conference on artificial intelligence, January 25–30, 2015, Austin, Texas, USA, pp 2750–2756

  • Memisevic R (2012) On multi-view feature learning. In: Proceedings of the 29th international conference on machine learning, ICML 2012, Edinburgh, Scotland, UK, June 26–July 1, 2012

  • Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems 14 [Neural information processing systems: natural and synthetic, NIPS 2001, December 3–8, 2001, Vancouver, British Columbia, Canada], pp 849–856

  • Nie F, Huang H, Cai X, Ding CHQ (2010) Efficient and robust feature selection via joint \(l_{2,1}\)-norms minimization. In: Advances in neural information processing systems 23: 24th annual conference on neural information processing systems 2010. Proceedings of a meeting held 6–9 December 2010, Vancouver, British Columbia, Canada, pp 1813–1821

  • Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14, New York, NY, USA, August 24–27, pp 977–986

  • Polito M, Perona P (2001) Grouping and dimensionality reduction by locally linear embedding. In: Advances in neural information processing systems 14 [Neural information processing systems: natural and synthetic, NIPS 2001, December 3–8, 2001, Vancouver, British Columbia, Canada], pp 1255–1262

  • Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905. https://doi.org/10.1109/34.868688

    Article  Google Scholar 

  • Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038. https://doi.org/10.1007/s00521-013-1362-6

    Article  Google Scholar 

  • von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416. https://doi.org/10.1007/s11222-007-9033-z

    Article  MathSciNet  Google Scholar 

  • Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B 40(6):1438–1446. https://doi.org/10.1109/TSMCB.2009.2039566

    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, July 27–31, 2014, Québec City, Québec, Canada, pp 2149–2155

  • Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544. https://doi.org/10.1109/TPAMI.2015.2417578

    Article  Google Scholar 

  • Yin Q, Wu S, Wang L (2015) Incomplete multi-view clustering via subspace learning. In: Proceedings of the 24th ACM international conference on information and knowledge management, CIKM 2015, Melbourne, VIC, Australia, October 19–23, 2015, pp 383–392. https://doi.org/10.1145/2806416.2806526

  • Zhang Z, Zha H (2004) Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J Sci Comput 26(1):313–338. https://doi.org/10.1137/S1064827502419154

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang S, Yu X, Sui Y, Zhao S, Zhang L (2015) Object tracking with multi-view support vector machines. IEEE Trans Multimed 17(3):265–278. https://doi.org/10.1109/TMM.2015.2390044

    Google Scholar 

  • Zhang C, Fu H, Liu S, Liu G, Cao X (2015) Low-rank tensor constrained multiview subspace clustering. In: 2015 IEEE international conference on computer vision, ICCV 2015, Santiago, Chile, December 7–13, 2015, pp 1582–1590. https://doi.org/10.1109/ICCV.2015.185

  • Zhou D, Burges CJC (2007) Spectral clustering and transductive learning with multiple views. In: Proceedings of the twenty-fourth international conference on machine learning (ICML 2007), Corvallis, Oregon, USA, June 20–24, 2007, pp 1159–1166. https://doi.org/10.1145/1273496.1273642

Download references

Acknowledgements

This study funded by the National Natural Science Foundation of China (61672364, 61672365, 61402310, 61373093).

Author information

Authors and Affiliations

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

    Hongwei Yin, Li Zhang & Zhao Zhang

  2. Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, China

    Fanzhang Li & Li Zhang

Authors
  1. Hongwei Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanzhang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongwei Yin.

Ethics declarations

Conflicts 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

Communicated by V. Loia.

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., Li, F., Zhang, L. et al. Multi-view clustering via spectral embedding fusion. Soft Comput 23, 343–356 (2019). https://doi.org/10.1007/s00500-018-3184-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3184-z

Keywords

Search

Navigation