Abstract
With the development of data collection techniques, multi-view clustering (MVC) becomes an emerging research direction to improve the clustering performance. However, most MVC methods assume that the objects are observed on all the views. As a result, existing MVC methods may not achieve satisfactory performance when some views are incomplete. In this paper, we propose a new MVC method, called as partial multi-view clustering via auto-weighting similarity completion (PMVC-ASC). The major contribution lies in jointly learning the consensus similarity matrix, exploring the complementary information among multiple distinct feature sets, quantifying the contribution of each view and splitting the similarity graph into several informative submatrices, each submatrix corresponding to one cluster. The learning process can be modeled via a joint minimization problem, and the corresponding optimization algorithm is given. A series of experiments are conducted on real-world datasets to demonstrate the superiority of PMVC-ASC by comparing with the state-of-the-art methods.
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References
Li, C., Vidal, R.: Structured sparse subspace clustering: a unified optimization framework. In: Proceedings of CVPR (2015)
Li, S., Jiang, Y., Zhou, Z.: Partial multi-view clustering. In: Proceedings of AAAI (2014)
Ye, Y., Liu, X., Liu, Q., Yin, J.: Consensus kernel-means clustering for incomplete multiview data. Comput. Intell. Neurosci. 2017, 11 (2017)
Rai, N., Neigi, S., Chaudhury, S.: Partial multi-view clustering using graph regularized NMF. In: Proceedings of ICPR (2016)
Yin, Q., Wu, S., Wang, L.: Unified subspace learning for incomplete and unlabeled multi-view data. Pattern Recognit. 67, 313–327 (2017)
Liu, X., Li, M., Wang, L., Dou, Y., Yin, J., Zhu, E.: Multiple Kernel k-means with incomplete kernels. In: Proceedings of AAAI (2017)
Shao, W., He, L., Yu, P.S.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with \(L_{2,1}\) regularization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 318–334. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_20
Trivedi, A., Rai, P., Daume, H.: Multiview clustering with incomplete views. In: Proceedings of NIPS (2010)
Shao, W., Shi, X., Yu, P.: Clustering on multiple incomplete datasets via collective kernel learning. In: Proceedings of ICDM (2013)
Zhao, L., Chen, Z., Yang, Y., Wang, Z., Leung, V.: Incomplete multi-view clustering via deep semantic mapping. Neurocomputing 275, 1053–1062 (2018)
Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of ACM SIGKDD (2014)
Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: Proceedings of IJCAI (2016)
Nie, F., Cai, G., Li, X.: Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of AAAI (2017)
Xu, C., Tao, D., Xu, C.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24(12), 5812–5825 (2015)
Bhadra, S., Kaski, S., Rousu, J.: Multi-view kernel completion. Mach. Learn. 106(5), 713–739 (2017)
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Min, C., Cheng, M., Yu, J., Jing, L. (2018). Partial Multi-view Clustering via Auto-Weighting Similarity Completion. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_23
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DOI: https://doi.org/10.1007/978-3-319-97909-0_23
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