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The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
Abstract
The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
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Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2330900 · Cited by 895
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
Nie, F., Wang, X., Jordan, M., & Huang, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10302 · Cited by 837
Metadata
- Authors
- Li, T., & Ding, C.
- Venue
- Sixth International Conference on Data Mining (ICDM'06)
- Publisher
- IEEE
- Published
- 2006-12
- Pages
- 362-371
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2124890708
- Cited by
- 315
- References
- 3
- FWCI
- 8.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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