Advertisement

Soft Computing

, Volume 23, Issue 1, pp 343–356 | Cite as

Multi-view clustering via spectral embedding fusion

  • Hongwei YinEmail author
  • Fanzhang Li
  • Li Zhang
  • Zhao Zhang
Methodologies and Application
  • 142 Downloads

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.

Keywords

Multi-view clustering Spectral clustering Structure information Spectral embedding fusion 

Notes

Acknowledgements

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

Compliance with ethical standards

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.

References

  1. 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 585–591Google Scholar
  2. 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
  3. 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 2532–2538Google Scholar
  4. 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 199–207Google Scholar
  5. 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
  6. 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 393–400Google Scholar
  7. 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 1413–1421Google Scholar
  8. 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 2750–2756Google Scholar
  9. 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, 2012Google Scholar
  10. 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 849–856Google Scholar
  11. 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 1813–1821Google Scholar
  12. 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 977–986Google Scholar
  13. 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 1255–1262Google Scholar
  14. 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 CrossRefGoogle Scholar
  15. 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 CrossRefGoogle Scholar
  16. von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416.  https://doi.org/10.1007/s11222-007-9033-z MathSciNetCrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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 2149–2155Google Scholar
  19. 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 CrossRefGoogle Scholar
  20. 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
  21. 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 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 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
  23. 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
  24. 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

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hongwei Yin
    • 1
    Email author
  • Fanzhang Li
    • 2
  • Li Zhang
    • 1
    • 2
  • Zhao Zhang
    • 1
  1. 1.College of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Joint International Research Laboratory of Machine Learning and Neuromorphic ComputingSoochow UniversitySuzhouChina

Personalised recommendations