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Kernel-independent component analysis

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Kernel independent component analysis (Kernel ICA) is an efficient algorithm for independent component analysis based on estimating source components, which are represented in a reproducing kernel Hilbert space based on optimizing a generalized variance contrast function.[1][2] Those contrast functions use the notion of mutual information as a measure of statistical independence.

Main idea

References

  1. ^ Bach, Francis R.; Jordan, Michael I. (2003). "Kernel independent component analysis" (PDF). The Journal of Machine Learning Research. 3: 1–48. doi:10.1162/153244303768966085.
  2. ^ Bach, Francis R.; Jordan, Michael I. (2003). "Kernel independent component analysis" (PDF). IEEE International Conference on Acoustics, Speech, and Signal Processing. doi:10.1109/icassp.2003.1202783.
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