The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Partial Multi-View Clustering
Open accessAbstract
Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples. In real tasks, however, it is oftenthe case that every view suffers from the missing ofsome data and therefore results in many partial examples,i.e., examples with some views missing. In this paper,we present possibly the first study on partial multiviewclustering. Our proposed approach, PVC, worksby establishing a latent subspace where the instancescorresponding to the same example in different viewsare close to each other, and similar instances (belongingto different examples) in the same view should bewell grouped. Experiments on two-view data demonstratethe advantages of our proposed approach.
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Cites
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Multi-view learning overview: Recent progress and new challenges
Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002
Late Fusion Incomplete Multi-View Clustering
Liu, X., Zhu, X., Li, M., Wang, L., Tang, C., Yin, J., Shen, D., Wang, H., & Gao, W. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2879108 · Cited by 363
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
NEMO: cancer subtyping by integration of partial multi-omic data
Rappoport, N., & Shamir, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz058 · Cited by 286
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
Metadata
- Authors
- Li, S.-Y., Jiang, Y., & Zhou, Z.-H.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-21
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2181159407
- Cited by
- 431
- References
- 29
- FWCI
- 8.50
- Type
- conference paper
- Language
- en
- Open access
- diamond
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