The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Nonsmooth nonnegative matrix factorization (nsNMF)
Abstract
We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly localized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.
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Cites
Learning the parts of objects by non-negative matrix factorization
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,007
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Self-citationNon-negative matrix factorization with sparseness constraints
Hoyer, P. O. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0408058 · Cited by 2,591
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
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Biclustering algorithms for biological data analysis: a survey
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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
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732
Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology
Devarajan, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000029 · Cited by 454
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing
Jia, S., & Qian, Y. (2009) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.2002882 · Cited by 369
Degasperi, A., Amarante, T. D., Czarnecki, J., Shooter, S., Zou, X., Glodzik, D., Morganella, S., Nanda, A. S., Badja, C., Koh, G., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0027-5 · Cited by 304
Metadata
- Authors
- Pascual-Montano, A., Carazo, J. M., Kochi, K., Lehmann, D., & Pascual-Marqui, R. D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2006-03
- Volume
- 28
- Issue
- 3
- Pages
- 403-415
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2146913572
- Cited by
- 419
- References
- 31
- FWCI
- 27.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pascual-Montano, A., Carazo, J. M., Kochi, K., Lehmann, D., & Pascual-Marqui, R. D. (2006). Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2006.60
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