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research-article

Evolutionary multi-objective optimization for multi-view clustering

Authors:

Liping WangAuthors Info & Claims

2016 IEEE Congress on Evolutionary Computation (CEC)

Pages 3308 - 3315

https://doi.org/10.1109/CEC.2016.7744208

Published: 01 July 2016 Publication History

Abstract

In some real-world applications, multiple measuring methods are often employed to extract multiple feature groups of data, yielding multi-view data. The main challenge of multiview clustering is to find a suitable way of simultaneously exploiting the complementary information of all views, considering the view conflicts arose by different measures. For perspective of optimization, previous multi-view clustering studies applied weighted sum method to represent degree of conflict and treated it as a weighted sum single-objective optimization problem. In this work, we formatted multi-view clustering as a multi-objective optimization problem, in which each view is regarded as a totally independent feature subset. The clustering objective function in each view is one of the multiple objectives. Five popular multi-objective evolutionary algorithms (MOEAs), i.e., NSGA-II, SPEA2, MOEA/D, SMS-EMOA and NSGA-III, were used to solve the induced multi-objective problem. Six real-world multi-view datasets were used to evaluate the proposed method and the experimental results showed that SPEA2 significantly outperformed the other MOEAs according to three performance evaluation indices.

References

[1]

A. Ramesh, D. Goldwasser, B. Huang, H. Daume III, and L. Getoor, “Learning latent engagement patterns of students in online courses,” in Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

[2]

H.-P. Kriegel, E. Schubert, and A. Zimek, “Evaluation of multiple clustering solutions,” in The 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with ECML PKDD, 2011.

[3]

S. Bickel and T. Scheffer, “Multi-view clustering,” in Proceedings of the Fourth IEEE International Conference on Data Mining, ser. ICDM ‘04. Washington, DC, USA: IEEE Computer Society, 2004, pp. 19–26.

[4]

V. R. de Sa, “Spectral clustering with two views,” in ICML workshop on learning with multiple views, 2005.

[5]

A. Kumar and H. Daumé III “A co-training approach for multiview spectral clustering,” in 28th InternationalConference on Machine Learning, Bellevue, Washington, 2011, pp. 393–400.

[6]

H. D. I. Abhishek Kumar, “A co-training approach for multi-view spectral clustering,” in Proceedings of the 28 th InternationalConference on Machine Learning. Bellevue, WA, USA, 2011, pp. 393–400.

[7]

J. Liu, C. Wang, J. Gao, and J. Han, “Multi-view clustering via joint nonnegative matrix factorization,” in Proc. SIAM Data Mining Conf.(SDM13), 2013.

[8]

S.-Y. Li, Y. Jiang, and Z.-H. Zhou, “Partial multi-view clustering,” in Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

[9]

Y. Li, F. Nie, H. Huang, and J. Huang, “Large-scale multi-view spectral clustering via bipartite graph,” 2015.

[10]

X. Zhang, L. Zong, X. Liu, and H. Yu, “Constrained nmf-based multiview clustering on unmapped data,” 2015.

[11]

S. Xiang, T. Yang, and J. Ye, “Simultaneous feature and feature group selection through hard thresholding,” in Proceedings of the 20th ACM SIGKDD internationalconference on Knowledge discovery and data mining, vol. 1, 2014, pp. 532–541.

[12]

B. Long, P. Yu, and Z. M. Zhang, “A general model for multiple view unsupervised learning,” in Proceedings of the 2008 SIAM internationalconference on data mining, 2008, pp. 822–833.

[13]

D. Greene and P. Cunningham, “A matrix factorization approach for integrating multiple data views,” in Machine Learning and Knowledge Discovery in Databases. Springer 2009, pp. 423–438.

[14]

E. Bruno and S. Marchand-Maillet, “Multiview clustering: A late fusion approach using latent models,” in Proceedings of the 32nd internationalACM SIGIR conference on Research and development in information retrieval. ACM, 2009, pp. 736–737.

[15]

X. Cai, F. Nie, and H. Huang, “Multi-view k-means clustering on big data,” in Proceedings of the Twenty-Third internationaljoint conference on Artificial Intelligence. AAAI Press, 2013, pp. 2598–2604.

[16]

X. Chen, Y. Ye, X. Xu, and J. Z. Huang, “A feature group weighting method for subspace clustering of high-dimensional data,” Pattern Recogn. vol. 45, no. 1, pp. 434–446, Jan. 2012.

Digital Library

[17]

X. Chen, X. Xu, J. Huang, and Y. Ye, “Tw-k-means: Automated two-level variable weighting clustering algorithm for multi-view data,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 932–944, 2013.

Digital Library

[18]

G. Tzortzis and A. Likas, “Kernel-based weighted multi-view clustering,” in IEEE 12th InternationalConference on Data Mining, 2012, pp. 675–684.

[19]

B. Jiang, F. Qiu, L. Wang, and Z. Zhang, “Bi-level weighted multiview clustering via hybrid particle swarm optimization,” Information Processing & Management, vol. 52, no. 3, pp. 387 — 398, 2016.

Digital Library

[20]

H. Wang, F. Nie, and H. Huang, “Multi-view clustering and feature learning via structured sparsity,” in Proceedings of the 30th InternationalConference on Machine Learning (ICML-13), S. Dasgupta and D. Mcallester, Eds., vol. 28, no. 3. JMLR Workshop and Conference Proceedings, 2013, pp. 352–360.

[21]

B. Cao, L. He, X. Kong, P. S. Yu, Z. Hao, and A. B. Ragin, “Tensor-based multi-view feature selection with applications to brain diseases,” in Data Mining (ICDM), 2014 IEEE InternationalConference on. IEEE, 2014, pp. 40–49.

[22]

K. Miettinen, Nonlinear multiobjective optimization. Springer Science& Business Media, 2012, vol. 12.

[23]

K. Chaudhuri, S. M. Kakade, K. Livescu, and K. Sridharan, “Multi-view clustering via canonical correlation analysis,” in Proceedings of the 26th Annual International Conference on Machine Learning, ser. ICML ‘09. New York, NY, USA: ACM, 2009, pp. 129–136.

[24]

G. F. Tzortzis and C. Likas, “Multiple view clustering using a weighted combination of exemplar-based mixture models,” IEEE Transactions on Neural Networks, vol. 21, no. 12, pp. 1925–1938, 2010.

Digital Library

[25]

X. Wang, B. Qian, J. Ye, and I. Davidson, “Multi-objective multi-view spectral clustering via pareto optimization.” SDM, 2013.

[26]

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” Evolutionary Computation, IEEE Transactions on, vol. 6, no. 2, pp. 182–197, 2002.

Digital Library

[27]

Z. Eckart, M. Laumanns, and L. Thiele, “Spea2: improving the strength pareto evolutionary algorithm,” Swiss Federal Institute of Technology, Technical Report, 2002.

[28]

N. Beume, B. Naujoks, and M. Emmerich, “Sms-emoa: Multiobjective selection based on dominated hypervolume,” European Journal of Operational Research, vol. 181, no. 3, pp. 1653 — 1669, 2007. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0377221706005443

[29]

Q. Zhang and H. Li, “Moea/d: A multiobjective evolutionary algorithm based on decomposition,” Evolutionary Computation, IEEE Transactions on, vol. 11, no. 6, pp. 712–731, 2007.

Digital Library

[30]

K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints,” Evolutionary Computation, IEEE Transactions on, vol. 18, no. 4, pp. 577–601, 2014.

[31]

J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, “The amsterdam library of object images,” International Journal of Computer Vision, vol. 61, no. 1, pp. 103–112, 2005.

Digital Library

Cited By

Ouadfel SAbd Elaziz M(2021)A multi-objective gradient optimizer approach-based weighted multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104480106:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.engappai.2021.104480
Mitra SHasanuzzaman MSaha S(2020)A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative ModelACM Transactions on Knowledge Discovery from Data10.1145/336567314:1(1-31)Online publication date: 3-Feb-2020
https://dl.acm.org/doi/10.1145/3365673
de Gusmão Rde Carvalho F(2019)Clustering of multi-view relational data based on particle swarm optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.12.053123:C(34-53)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1016/j.eswa.2018.12.053

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2016 IEEE Congress on Evolutionary Computation (CEC)

5624 pages

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Published: 01 July 2016

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Cited By

Ouadfel SAbd Elaziz M(2021)A multi-objective gradient optimizer approach-based weighted multi-view clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104480106:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.engappai.2021.104480
Mitra SHasanuzzaman MSaha S(2020)A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative ModelACM Transactions on Knowledge Discovery from Data10.1145/336567314:1(1-31)Online publication date: 3-Feb-2020
https://dl.acm.org/doi/10.1145/3365673
de Gusmão Rde Carvalho F(2019)Clustering of multi-view relational data based on particle swarm optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.12.053123:C(34-53)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1016/j.eswa.2018.12.053

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