Blind Source Separation of Natural Signals Based on Approximate Complexity Minimization

@inproceedings{Pajunen1999BlindSS,
  title={Blind Source Separation of Natural Signals Based on Approximate Complexity Minimization},
  author={Petteri Pajunen},
  year={1999}
}
An approach to blind source separation is presented based on minimizing complexity. The diiculty of measuring complexity of signals is dealt with by assuming that the signals are Gaussian, time-correlated stochas-tic processes. In a special case, this approach coincides with previously proposed methods where the separating solution is obtained from the eigendecomposition of a cross-correlation matrix. 

From This Paper

Topics from this paper.
13 Citations
14 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 14 references

Rotating machine vi- bration analysis using second-order independent component analysis, in ICA'99 Workshop, Aus- sois

  • A. Ypma, P. Pajunen
  • 1999

A general independent component analysis framework based on bayesian- kullback ying-yang learning, in Progress in Neural Information Processing

  • L. Xu, S. Amari
  • Proc. Intl. Conf. on Neu- ral Information…
  • 1996
1 Excerpt

Vigario, Principal and independent components in neural networks recent developments, in Proc. of the 7th Italian Workshop on Neural Networks (WIRN- 95)

  • E. Oja, J. Karhunen, L. Wang
  • (Vietri sul Mare, Italy),
  • 1995
1 Excerpt

Rummert, Robust learning algorithm for blind separation of signals

  • A. Cichocki, R. Unbehauen
  • Electronics Letters,
  • 1994
1 Excerpt

Separation of inde- pendent signals using time-delayed correlations

  • L. Molgedy, H. Schuster
  • Physical Review Letters,
  • 1994
1 Excerpt

Similar Papers

Loading similar papers…