The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Orthogonal nonnegative matrix t-factorizations for clustering
Abstract
Currently, most research on nonnegative matrix factorization (NMF)focus on 2-factor $X=FG^T$ factorization. We provide a systematicanalysis of 3-factor $X=FSG^T$ NMF. While it unconstrained 3-factor NMF is equivalent to it unconstrained 2-factor NMF, itconstrained 3-factor NMF brings new features to it constrained 2-factor NMF. We study the orthogonality constraint because it leadsto rigorous clustering interpretation. We provide new rules for updating $F,S, G$ and prove the convergenceof these algorithms. Experiments on 5 datasets and a real world casestudy are performed to show the capability of bi-orthogonal 3-factorNMF on simultaneously clustering rows and columns of the input datamatrix. We provide a new approach of evaluating the quality ofclustering on words using class aggregate distribution andmulti-peak distribution. We also provide an overview of various NMF extensions andexamine their relationships.
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Cites
Learning the parts of objects by non-negative matrix factorization
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Non-negative matrix factorization with sparseness constraints
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Metagenes and molecular pattern discovery using matrix factorization
Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183
Probabilistic Latent Semantic Analysis
Hofmann, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6705 · Cited by 2,047
Document clustering based on non-negative matrix factorization
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Cited by
Nonnegative Matrix Factorization: A Comprehensive Review
Wang, Y.-X., & Zhang, Y.-J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.51 · Cited by 981
Transfer Learning for Visual Categorization: A Survey
Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2330900 · Cited by 895
SVD based initialization: A head start for nonnegative matrix factorization
Boutsidis, C., & Gallopoulos, E. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.09.010 · Cited by 695
A Review of Generalized Zero-Shot Learning Methods
Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C. P., Wang, X.-Z., & Wu, Q. M. J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3191696 · Cited by 473
Kim, J., He, Y., & Park, H. (2014) · Journal of Global Optimization · doi:10.1007/s10898-013-0035-4 · Cited by 352
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Metadata
- Authors
- Ding, C., Li, T., Peng, W., & Park, H.
- Venue
- Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2006-08-20
- Pages
- 126-135
- OpenAlex
- W2043545458
- Cited by
- 1,245
- References
- 27
- FWCI
- 23.75
- Type
- conference paper
- Language
- en
- Open access
- closed
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