Abstract
In many clustering problems, there are dozens of data which are represented by multiple views. Different views describe different aspects of the same set of instances and provide complementary information. Considering blindly combining the information from different views will degrade the multi-view clustering result, this paper proposes a novel view-weighted multi-view k-means method. Meanwhile, to reduce the adverse effect of outliers, \(l_{2,1}\) norm is employed to calculate the distance between data points and cluster centroids. An alternative iterative update schema is developed to find the optimal value. Comparative experiments on real world datasets reveal that the proposed method has better performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDM, pp. 19–26 (2004)
Cai, X., Nie, F., Huang, H.: Multi-view k-means clustering on big data. In: IJCAI, pp. 2598–2604 (2013)
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML, pp. 129–136 (2009)
Chikhi, N.F.: Multi-view clustering via spectral partitioning and local refinement. Inf. Process. Manag. 52(4), 618–627 (2016)
Ding, C., He, X., Simon, H.D.: Nonnegative Lagrangian relaxation of K-means and spectral clustering. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 530–538. Springer, Heidelberg (2005). doi:10.1007/11564096_51
Du, L., Zhou, P., Shi, L., Wang, H., Fan, M.: Robust multiple kernel k-means using l21-norm. In: IJCAI (2015)
Ioannidis, A., Chasanis, V., Likas, A.: Key-frame extraction using weighted multi-view convex mixture models and spectral clustering. In: ICPR, pp. 3463–3468 (2014)
Jiang, B., Qiu, F., Wang, L.: Multi-view clustering via simultaneous weighting on views and features. Appl. Soft Comput. 47, 304–315 (2016)
Kumar, A., Daumé, H.: A co-training approach for multi-view spectral clustering. In: ICML, pp. 393–400 (2011)
Kumar, A., Rai, P., Daumé, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1413–1421 (2011)
Son, J.W., Jeon, J., Lee, A., Kim, S.J.: Spectral clustering with brainstorming process for multi-view data. In: AAAI (2017)
Sun, J., Lu, J., Xu, T., Bi, J.: Multi-view sparse co-clustering via proximal alternating linearized minimization. In: ICML, pp. 757–766 (2015)
Sun, S.: A survey of multi-view machine learning. Neural Comput. Appl. 23(7–8), 2031–2038 (2013)
Tzortzis, G., Likas, A.: Kernel-based weighted multi-view clustering. In: ICDM, pp. 675–684 (2012)
Xia, R., Pan, Y., Du, L., Yin, J.: Robust multi-view spectral clustering via low-rank and sparse decomposition. In: AAAI, pp. 2149–2155 (2014)
Xu, Y.M., Wang, C.D., Lai, J.H.: Weighted multi-view clustering with feature selection. Pattern Recogn. 53, 25–35 (2016)
Acknowledgment
Research reported in this publication was supported by the National Natural Science Foundation of China (61602081) and Natural Science Foundation of Liaoning Province (201602180).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, H., Lian, Y., Li, S., Chen, J. (2017). View-Weighted Multi-view K-means Clustering. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-68612-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68611-0
Online ISBN: 978-3-319-68612-7
eBook Packages: Computer ScienceComputer Science (R0)