Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tpl2go/awesome-ICA
A list of resources related to Independent Component Analysis
https://github.com/tpl2go/awesome-ICA
List: awesome-ICA
Last synced: 16 days ago
JSON representation
A list of resources related to Independent Component Analysis
- Host: GitHub
- URL: https://github.com/tpl2go/awesome-ICA
- Owner: tpl2go
- Created: 2020-01-24T16:37:46.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-20T16:10:14.000Z (almost 5 years ago)
- Last Synced: 2024-04-11T08:00:53.181Z (8 months ago)
- Size: 3.91 KB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-ICA - A list of resources related to Independent Component Analysis. (Other Lists / Monkey C Lists)
README
# awesome-ICA
A list of resources related to Independent Component Analysis
## Licence and Contributing
[](http://creativecommons.org/publicdomain/zero/1.0/)
**awesome-ICA** is released under Public Domain. Feel free to complete and/or correct any of these lists.
[Pull requests](https://github.com/tpl2go/awesome-ICA/pulls) are very welcome!
## Algorithms
### Algebraic Methods
- [JADE](https://pdfs.semanticscholar.org/1e74/ddf23992e14182f42b173e673546824996eb.pdf)
- [COM2](https://hal.archives-ouvertes.fr/hal-00417283/document)
- [STOTD](https://hal.archives-ouvertes.fr/hal-00417283/document)
- [COM1](https://www2.spsc.tugraz.at/people/franklyn/ICASSP97/pdf/author/ic973453.pdf)
### Mutual Information
- [Kernel ICA](https://www.di.ens.fr/~fbach/kernelICA-jmlr.pdf)
### Entropy
- [FastICA](https://www.cs.helsinki.fi/u/ahyvarin/papers/TNN99_reprint.pdf)
- [complex-FastICA](https://www.cs.helsinki.fi/u/ahyvarin/papers/IJNS00.pdf)
- [nc-FastICA](https://ieeexplore.ieee.org/document/4454224)
- [Picard-O: Faster ICA under orthogonal constraint](https://hal.inria.fr/hal-01651842)
- [RADICAL](https://people.cs.umass.edu/~elm/papers/learned-miller03a.pdf)
- [InfoMax ICA](http://www.inf.fu-berlin.de/lehre/WS05/Mustererkennung/infomax/infomax.pdf)
- [Entropy-Bound Methods](http://mlsp.umbc.edu/ica_ebm.html)
- [ICA-EBM](https://ieeexplore.ieee.org/document/5499122)
- [ICA-ERBM](https://ieeexplore.ieee.org/abstract/document/5495311)
- [ICA-ERM](https://ieeexplore.ieee.org/document/6845364)
### ML-Estimation
- [KDICA: Kernel Density ICA](https://link.springer.com/chapter/10.1007/11679363_4)
- [EMICA](https://www.ics.uci.edu/~welling/classnotes/papers_class/ICA.ps.gz)
### Cumulants
- [JADE: Joint Approximate Diagonalization of Eigen-matrices](https://pdfs.semanticscholar.org/1e74/ddf23992e14182f42b173e673546824996eb.pdf)
- [FOBI: Fourth Order Blind Identification](https://ieeexplore.ieee.org/document/266878)
### Others
- Reconstruction ICA
- Orthonormal ICA
### For communications signals
- [T-CMN](https://ieeexplore.ieee.org/document/4443874)
- [A-CMN](https://ieeexplore.ieee.org/document/4053625)
- [C-QAM](https://ieeexplore.ieee.org/document/4217441)
### Adaptive
- [ORICA: Online recursive independent component analysis for real-time source separation of high-density EEG](https://ieeexplore.ieee.org/abstract/document/6944462)
-[EASI: equivariant adaptive separation by independence](https://ieeexplore.ieee.org/document/553476)
-
### Advanced Algorithms
- [TS-FPICA: two-stage fixed-point ICA](http://journal13.magtechjournal.com/Jwk_yddxen/fileup/PDF/2015wz-042.pdf)
-
## Comparative Studies
- [High-Order Contrasts forIndependent Component Analysis](http://www2.iap.fr/users/cardoso/papers/neuralcomp_2ppf.pdf)
## Code
- [ORICA](https://github.com/goodshawn12/orica)
- [sklearn FastICA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html)
- [Machine Learning for Signal Processing Lab](http://mlsp.umbc.edu/resources.html)
- [complex ICA](https://github.com/afbujan/complex_ica/)
-
## Talks
- [Independent Component Analysis: From Theory to Practice and Back](https://www.youtube.com/watch?v=KSIA908KNiw)
-
## Books
- Blind Source Separation: Theory and Applications
## Tutorials
- [A Tutorial on Independent Component Analysis](https://arxiv.org/abs/1404.2986)
- [UFLDL Tutorials](http://ufldl.stanford.edu/tutorial/unsupervised/ICA/)
- [Blind Source Separation: statistical principles](http://www2.iap.fr/users/cardoso/papers/ProcIEEE.pdf)
## Theses
- [New Approach to Complex Valued ICA: From FOBI to AMUSE](https://sal.aalto.fi/publications/pdf-files/tlie16_public.pdf)
## Famous People
- [Piere Comon](http://www.gipsa-lab.grenoble-inp.fr/~pierre.comon/)
- [Jean-François Cardoso](http://www2.iap.fr/users/cardoso/)