The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multi-View Clustering
Abstract
We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks); multi-view learning has so far been studied in the context of classification. We develop and study partitioning and agglomerative, hierarchical multi-view clustering algorithms for text data. We find empirically that the multi-view versions of k-means and EM greatly improve on their single-view counterparts. By contrast, we obtain negative results for agglomerative hierarchical multi-view clustering. Our analysis explains this surprising phenomenon.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
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
A survey of multi-view machine learning
Sun, S. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1362-6 · Cited by 878
Progress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., & Han, J. (2016) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2897350.2897352 · Cited by 336
Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis
Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102
Metadata
- Authors
- Bickel, S., & Scheffer, T.
- Venue
- Fourth IEEE International Conference on Data Mining (ICDM'04)
- Publisher
- IEEE
- Published
- 2005-03-31
- Pages
- 19-26
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2108502868
- Cited by
- 710
- References
- 1
- FWCI
- 6.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bickel, S., & Scheffer, T. (2005). Multi-View Clustering. Fourth IEEE International Conference on Data Mining (ICDM'04). https://doi.org/10.1109/icdm.2004.10095
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
