Imposing sparsity on the mixing matrix in independent component analysis

  title={Imposing sparsity on the mixing matrix in independent component analysis},
  author={Aapo Hyv{\"a}rinen and Karthikesh Raju},
In independent component analysis, prior information on the distributions of the independent components is often used; some weak information may in fact be necessary for successful estimation. In contrast, prior information on the mixing matrix is usually not used. This is because it is considered that the estimation should be completely blind as to the form of the mixing matrix. Nevertheless, it could be possible to 1nd forms of prior information that are su2ciently general to be useful in a… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 25 references

A fast 1 xedpoint algorithm for independent component analysis

  • E. Oja
  • Sparse priors on the mixing matrix in independent…
  • 2000

Independent component analysis: algorithms and applications, Neural Networks

  • A. Hyv arinen, E. Oja
  • Hyv- arinen, K. Raju /Neurocomputing
  • 2000
1 Excerpt

Karthikesh, Sparse priors on the mixing matrix in independent component analysis, Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000)

  • R. A. Hyv arinen
  • 2000
1 Excerpt

Prior information about mixing matrix in BSS-ICA formulation

  • J. Igual, L. Vergara
  • Proceedings of the International Workshop on…
  • 2000
1 Excerpt

Hyv % arinen , Fast and robust 1 xedpoint algorithms for independent component analysis

  • A.
  • IEEE Trans . Neural Networks
  • 1999

Hyv % arinen , Sparse code shrinkage : Denoising of nongaussian data by maximum likelihood estimation

  • P. O. Hoyer
  • Neural Comput .
  • 1999


  • J. V. Stone, J. Porrill, C. Buchel, K. Friston
  • temporal, and spatiotemporal independent…
  • 1999
3 Excerpts

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