Least-squares Independent Component Analysis

Least-squares Independent Component Analysis (LICA) is a method of Independent Component Analysis (ICA) that utilizes a squared-loss variant of mutual information as an independence measure.

Matlab Implementation: LICA.zip

LICA.m is the main function.
- LICA.m calls IsCalc.m, CalcGLICA.m, CompIsCVLICA.m, CVinLICA.m, CVVal.m.
demo.m is a demo script.

Examples

References

Suzuki, T. & Sugiyama, M:
Sufficient dimension reduction via squared-loss mutual information estimation.
Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS2010).
Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, and Jun Sese:
Mutual information estimation reveals global associations between stimuli and biological processes.
BMC Bioinformatics, 10(Suppl 1):S52, 2009.
Suzuki, T., Sugiyama, M., Sese, J., & Kanamori, T.:
A least-squares approach to mutual information estimation with application in variable selection.
In Proceedings of the 3rd workshop on new challenges for feature selection in data mining and knowledge discovery (FSDM2008). Antwerp, Belgium, 2008.