The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Kernel independent component analysis
Abstract
We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms.
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
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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
Cited by
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Multi-view learning overview: Recent progress and new challenges
Zhao, J., Xie, X., Xu, X., & Sun, S. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.007 · Cited by 1,002
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Sejdinovic, D., Sriperumbudur, B., Gretton, A., & Fukumizu, K. (2013) · The Annals of Statistics · doi:10.1214/13-aos1140 · Cited by 582
“(2008): we generated univariate random variables from the Independent Component Analysis (ICA) benchmark densities of Bach and Jordan (2002); rotated them in the product space by an angle between 0 and π/4 to introduce dependence; filled additional dimensions with independent Gaussian noise; and, finally, passed the r…”
MethodBerg, T. L., Berg, A. C., Edwards, J., Maire, M., White, R., Teh, Y.-W., Learned-Miller, E., & Forsyth, D. A. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315253 · Cited by 339
An, S., Liu, W., & Venkatesh, S. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.12.015 · Cited by 301
Metadata
- Authors
- Bach, F. R., & Jordan, M. I.
- Venue
- 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
- Publisher
- IEEE
- Published
- 2004-01-23
- Volume
- 4
- Pages
- IV-876-9
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1588424744
- Cited by
- 568
- References
- 2
- FWCI
- 24.46
- Type
- conference paper
- 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: Kernel independent component analysis. https://app.cochat.ai/papers/10.1109/icassp.2003.1202783
- The paper
- Bach, F. R., & Jordan, M. I. (2004). Kernel independent component analysis. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). https://doi.org/10.1109/icassp.2003.1202783
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.
