The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Fast Fixed-Point Algorithm for Independent Component Analysis
Abstract
We introduce a novel fast algorithm for independent component analysis, which can be used for blind source separation and feature extraction. We show how a neural network learning rule can be transformed into a fixedpoint iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The algorithm finds, one at a time, all nongaussian independent components, regardless of their probability distributions. The computations can be performed in either batch mode or a semiadaptive manner. The convergence of the algorithm is rigorously proved, and the convergence speed is shown to be cubic. Some comparisons to gradient-based algorithms are made, showing that the new algorithm is usually 10 to 100 times faster, sometimes giving the solution in just a few iterations.
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Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,205
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture
Jutten, C., & Herault, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90079-x · Cited by 2,521
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
A New Learning Algorithm for Blind Signal Separation
Амари, Ш.-И., Cichocki, A. S., & Yang, H. H. (1995) · Cited by 1,904
Adaptive blind separation of independent sources: A deflation approach
Delfosse, N., & Loubaton, P. (1995) · Signal Processing · doi:10.1016/0165-1684(95)00042-c · Cited by 499
Super-exponential methods for blind deconvolution
Shalvi, O., & Weinstein, E. (1993) · IEEE Transactions on Information Theory · doi:10.1109/18.212280 · Cited by 337
A family of fixed-point algorithms for independent component analysis
Hyvärinen, A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.604766 · Cited by 199
Self-citationLearning the higher-order structure of a natural sound
Bell, A. J., & Sejnowski, T. J. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/2/005 · Cited by 179
Image Feature Extraction Using Independent Component Analysis
Hurri, J., Hyvärinen, A., Karhunen, J., et al. (1996) · Cited by 48
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Cited by
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Lee, T.-W., Girolami, M., & Sejnowski, T. J. (1999) · Neural Computation · doi:10.1162/089976699300016719 · Cited by 1,967
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770
Metadata
- Authors
- Hyvärinen, A., & Oja, E.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1997-10-01
- Volume
- 9
- Issue
- 7
- Pages
- 1483-1492
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2019502123
- Cited by
- 3,391
- References
- 10
- FWCI
- 70.30
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
