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Non-negative Matrix Factorization on Manifold
Abstract
Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. In this paper, we develop a graph based approach for parts-based data representation in order to overcome this limitation. We construct an affinity graph to encode the geometrical information and seek a matrix factorization which respects the graph structure. We demonstrate the success of this novel algorithm by applying it on real world problems.
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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
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
Tumor stratification by a novel graph-regularized bi-clique finding algorithm
Ahmadi Adl, A., & Qian, X. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.02.010 · Cited by 3
Metadata
- Authors
- Cai, D., He, X., Wu, X., & Han, J.
- Venue
- 2008 Eighth IEEE International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2008-12
- Pages
- 63-72
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2167686991
- Cited by
- 405
- References
- 2
- FWCI
- 13.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cai, D., He, X., Wu, X., & Han, J. (2008). Non-negative Matrix Factorization on Manifold. 2008 Eighth IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2008.57
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