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
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
Independent Component Analysis
(2004) · Independent Component Analysis · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,903
Paatero, P., & Tapper, U. (1994) · Environmetrics · doi:10.1002/env.3170050203 · Cited by 6,023
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,007
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994) · International Journal of Psychophysiology · doi:10.1016/0167-8760(84)90014-x · Cited by 2,869
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
Biclustering of expression data.
Cheng, Y., & Church, G. M. (2000) · PubMed · Cited by 2,098
Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049
Independent Component Analysis: Principles and Practice
(2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511624148 · Cited by 1,917
Non-negative matrix factorization for polyphonic music transcription
Smaragdis, P., & Brown, J. C. (2004) · 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684) · doi:10.1109/aspaa.2003.1285860 · Cited by 860
Discovering statistically significant biclusters in gene expression data
Tanay, A., Sharan, R., & Shamir, R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s136 · Cited by 855
Hoyer, P. O. (2003) · Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing · doi:10.1109/nnsp.2002.1030067 · Cited by 804
Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions
Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003) · Genome Research · doi:10.1101/gr.648603 · Cited by 726
Review of methods for solving the EEG inverse problem
Pascual-Marqui, R. D. (1999) · Cited by 700
Self-citationWhen Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Donoho, D. L., & Stodden, V. (2004) · doi:10.7916/d88d05n7 · Cited by 685
Mulert, C., Jäger, L., Schmitt, R., Bussfeld, P., Pogarell, O., Möller, H.-J., Juckel, G., & Hegerl, U. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.10.051 · Cited by 606
Subsystem Identification Through Dimensionality Reduction of Large-Scale Gene Expression Data
Kim, P. M., & Tidor, B. (2003) · Genome Research · doi:10.1101/gr.903503 · Cited by 251
Biclustering microarray data by Gibbs sampling
Sheng, Q., Moreau, Y., & De Moor, B. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1078 · Cited by 220
Biclustering Algorithms: A Survey
(2005) · Handbook of Computational Molecular Biology · doi:10.1201/9781420036275-40 · Cited by 200
Two-mode clustering methods: astructuredoverview
Van Mechelen, I., Bock, H.-H., & De Boeck, P. (2004) · Statistical Methods in Medical Research · doi:10.1191/0962280204sm373ra · Cited by 194
Improved biclustering of microarray data demonstrated through systematic performance tests
Turner, H., Bailey, T., & Krzanowski, W. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.02.003 · Cited by 165
Yao, J., & Dewald, J. P. A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.036 · Cited by 161
Non-negative Matrix Factorization for Face Recognition
Guillamet, D., & Vitria, J. M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36079-4_29 · Cited by 147
Local non-negative matrix factorization as a visual representation
Feng, T., Li, S. Z., Shum, H., & Zhang, H. (2003) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2002.1011835 · Cited by 98
Development of low entropy coding in a recurrent network
Harpur, G. F., & Prager, R. W. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/2/007 · Cited by 91
Color categories revealed by non-negative matrix factorization of Munsell color spectra
Buchsbaum, G., & Bloch, O. G. (2002) · Vision Research · doi:10.1016/s0042-6989(01)00303-0 · Cited by 76
Sensitive pattern discovery with ‘fuzzy’ alignments of distantly related proteins
Heger, A., & Holm, L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1017 · Cited by 49
Eigenviews for object recognition in multispectral imaging systems
Ramanath, R., Snyder, W. E., & Qi, H. (2004) · Proceedings · doi:10.1109/aipr.2003.1284245 · Cited by 28
Mel, B. W. (1999) · Nature · doi:10.1038/44507 · Cited by 28
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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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