Abstract
Multi-view clustering has attracted considerable attention in the past decades, due to its good performance on the data with multiple modalities or from diverse sources. In real-world applications, multi-view data often suffer from incompleteness of instances. Clustering on such multi-view data is called incomplete multi-view clustering (IMC). Most of the existing IMC solutions are offline and have high computational and memory costs especially for large-scale datasets. To tackle these challenges, in this paper, we propose a Online Binary Incomplete Multi-view Clustering (OBIMC) framework. OBIMC robustly learns the common compact binary codes for incomplete multi-view features. Moreover, the cluster structures are optimized with the binary codes in an online fashion. Further, we develop an iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Experiments on four real datasets demonstrate the efficiency and effectiveness of the proposed OBIMC method. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time.
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: IEEE International Conference on Data Mining (2004)
Ding, C.H.Q., He, X., Simon, H.D., Jin, R.: On the equivalence of nonnegative matrix factorization and k-means | spectral clustering. Lawrence Berkeley National Laboratory (2005)
Gao, H., Nie, F., Li, X., Huang, H.: Multi-view subspace clustering. In: IEEE International Conference on Computer Vision (2015)
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Hu, M., Chen, S.: One-pass incomplete multi-view clustering. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, pp. 3838–3845 (2019)
Kumar, A., Iii, H.D.: A co-training approach for multi-view spectral clustering abhishek kumar. In: International Conference on International Conference on Machine Learning (2011)
Kumar, A., Rai, P., Daumé, H.: Co-regularized multi-view spectral clustering. In: International Conference on Neural Information Processing Systems (2011)
Li, S., Jiang, Y., Zhou, Z.: Partial multi-view clustering. In: AAAI Conference on Artificial Intelligence, vol. 3, pp. 1968–1974, January 2014
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 252–260 (2013). https://doi.org/10.1137/1.9781611972832.28. https://epubs.siam.org/doi/abs/10.1137/1.9781611972832.28
Liu, X., Li, M., Wang, L., Dou, Y., Yin, J., Zhu, E.: Multiple kernel k-means with incomplete kernels. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 2259–2265. AAAI’2017, AAAI Press (2017). http://dl.acm.org/citation.cfm?id=3298483.3298565
Liu, X., Zhu, X., Li, M., Wang, L., Gao, W.: Late fusion incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(10), 2410–2423 (2018)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Nie, F., Cai, G., Li, J., Li, X.: Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Trans. Image Process. 27(3), 1501–1511 (2017)
Ren, P., et al.: Robust auto-weighted multi-view clustering. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 2644–2650, July 2018. https://doi.org/10.24963/ijcai.2018/367
Shao, W., He, L., Lu, C.T., Yu, P.S.: Online multi-view clustering with incomplete views. In: International Conference on Big Data, pp. 1012–1017 (2016)
Shao, W., He, L., Yu, P.S.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with \(\ell _{2,1}\) regularization. In: ECML PKDD, pp. 318–334 (2015)
Shen, F., Zhou, X., Yang, Y., Song, J., Shen, H.T., Tao, D.: A fast optimization method for general binary code learning. IEEE Trans. Image Process. 25(12), 5610–5621 (2016)
Tao, Z., Liu, H., Li, S., Ding, Z., Fu, Y.: From ensemble clustering to multi-view clustering. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 2843–2849 (2017). https://doi.org/10.24963/ijcai.2017/396
Wang, H., Zong, L., Liu, B., Yang, Y., Zhou, W.: Spectral perturbation meets incomplete multi-view data. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 3677–3683. International Joint Conferences on Artificial Intelligence Organization, July 2019. https://doi.org/10.24963/ijcai.2019/510
Wen, J., Zhang, Z., Xu, Y., Zhong, Z.: Incomplete multi-view clustering via graph regularized matrix factorization. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)
Xiao, C., Nie, F., Huang, H.: Multi-view k-means clustering on big data. In: International Joint Conference on Artificial Intelligence (2013)
Xu, C., Tao, D., Xu, C.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24(12), 5812–5825 (2015)
Yang, W., Xuemin, L., Lin, W., Wenjie, Z., Qing, Z., Xiaodi, H.: Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans. Image Process. 24(11), 3939–49 (2015)
Zhang, Z., Liu, L., Shen, F., Shen, H.T., Shao, L.: Binary multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1774–1782 (2018)
Zhao, H., Ding, Z., Fu, Y.: Multi-view clustering via deep matrix factorization. In: AAAI Conference on Artificial Intelligence (2017). https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14647
Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16, pp. 2392–2398 (2016)
Acknowledgment
This work is supported by the Major Research plan of the National Natural Science Foundation of China (Grant No.91648204), National Key Research and Development Project (Grant No.2017YFB1300203), and National Natural Science Foundation of China (Grant No.61973313).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, L., Zhang, L., Tang, Y. (2021). Online Binary Incomplete Multi-view Clustering. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12457. Springer, Cham. https://doi.org/10.1007/978-3-030-67658-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-67658-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67657-5
Online ISBN: 978-3-030-67658-2
eBook Packages: Computer ScienceComputer Science (R0)