The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Late Fusion Incomplete Multi-View Clustering
Open accessAbstract
Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel $k$k-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint optimization problem where the imputation and clustering are alternatively performed until convergence. However, the comparatively intensive computational and storage complexities preclude it from practical applications. To address these issues, we propose Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generated by incomplete views. Specifically, our algorithm jointly learns a consensus clustering matrix, imputes each incomplete base matrix, and optimizes the corresponding permutation matrices. We develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed LF-IMVC in terms of clustering accuracy, running time, advantages of late fusion multi-view clustering, evolution of the learned consensus clustering matrix, parameter sensitivity and convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.
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Cites
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,349
A Kernel Method for the Two-Sample-Problem
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Large-Scale Multi-View Spectral Clustering via Bipartite Graph
Li, Y., Nie, F., Huang, H., & Huang, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9598 · Cited by 585
Supervised learning from incomplete data via an EM approach
Zoubin Ghahramani, A., & Jordan, M. I. (1993) · Cited by 552
Multi-view K-means clustering on big data
Cai, X., Nie, F., & Huang, H. (2013) · International Joint Conference on Artificial Intelligence · Cited by 524
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Shao, W., He, L., & Yu, P. S. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-23528-8_20 · Cited by 281
Multi-View Learning With Incomplete Views
Xu, C., Tao, D., & Xu, C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2490539 · Cited by 260
Optimized Data Fusion for Kernel k-Means Clustering
Yu, S., Tranchevent, L.-C., Liu, X., Glanzel, W., Suykens, J. A. K., De Moor, B., & Moreau, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.255 · Cited by 231
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Cited by
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., & Zhu, E. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3131941 · Cited by 517
Self-citationA roadmap for multi-omics data integration using deep learning
Kang, M., Ko, E., & Mersha, T. B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab454 · Cited by 389
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Multiple Kernel k-means with Incomplete Kernels
Liu, X., Gao, W., Zhu, X., Li, M., Wang, L., Zhu, E., Liu, T., Kloft, M., Shen, D., & Yin, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2892416 · Cited by 300
Self-citationA Review of Integrative Imputation for Multi-Omics Datasets
Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127
Metadata
- Authors
- Liu, X., Zhu, X., Li, M., Wang, L., Tang, C., Yin, J., Shen, D., Wang, H., & Gao, W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-10-01
- Volume
- 41
- Issue
- 10
- Pages
- 2410-2423
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2899604627
- Cited by
- 363
- References
- 26
- FWCI
- 17.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, X., Zhu, X., Li, M., Wang, L., Tang, C., Yin, J., Shen, D., Wang, H., & Gao, W. (2019). Late Fusion Incomplete Multi-View Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2018.2879108
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