The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Non-negative sparse coding
Open accessAbstract
Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. We briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization. We then give a simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components. In addition, we show how the basis vectors can be learned from the observed data. Simulations demonstrate the effectiveness of the proposed method.
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Cites
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
Cited by
Non-negative matrix factorization with sparseness constraints
Hoyer, P. O. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0408058 · Cited by 2,591
Self-citationProjected Gradient Methods for Nonnegative Matrix Factorization
Lin, C.-J. (2007) · Neural Computation · doi:10.1162/neco.2007.19.10.2756 · Cited by 1,737
Witten, D. M., Tibshirani, R., & Hastie, T. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp008 · Cited by 1,535
Algorithms and applications for approximate nonnegative matrix factorization
Berry, M. W., Browne, M., Langville, A. N., Pauca, V. P., & Plemmons, R. J. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.11.006 · Cited by 1,512
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
Metadata
- Authors
- Hoyer, P. O.
- Venue
- Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
- Publisher
- IEEE
- Published
- 2003-06-25
- Pages
- 557-565
- OpenAlex
- W2116216716
- Cited by
- 804
- References
- 1
- FWCI
- 53.89
- Type
- conference paper
- Language
- en
- Open access
- green
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