The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fast and robust fixed-point algorithms for independent component analysis
Abstract
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast (objective) functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
Huber, P. J. (1985) · The Annals of Statistics · doi:10.1214/aos/1176349519 · Cited by 1,277
Cited by
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,531
“To automatically nominate a value for K, we took advantage of the fact that the fastICA algorithm begins with a whitening step, in which a singular value decomposition is used to select the top K eigenvectors (i.e., principal components) for maximization of non-Gaussianity (Hyvärinen, 1999).”
BackgroundMethodInfluentialA Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals
Bingham, E. (2000) · International Journal of Neural Systems · doi:10.1016/s0129-0657(00)00002-8 · Cited by 854
Genome-wide association studies of brain imaging phenotypes in UK Biobank
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J., & Smith, S. M. (2018) · Nature · doi:10.1038/s41586-018-0571-7 · Cited by 834
Metadata
- Authors
- Hyvarinen, A.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1999-05
- Volume
- 10
- Issue
- 3
- Pages
- 626-634
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2141224535
- Cited by
- 6,246
- References
- 3
- FWCI
- 90.76
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fast and robust fixed-point algorithms for independent component analysis. https://app.cochat.ai/papers/10.1109/72.761722
- The paper
- Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.761722
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
