ML Anthology

Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring

Howard Hua Yang
NeurIPS 1997 pp. 696-702
/neurips/1997/yang1997neurips-multiplicative/

Abstract

For blind source separation, when the Fisher information matrix is used as the Riemannian metric tensor for the parameter space, the steepest descent algorithm to maximize the likelihood function in this Riemannian parameter space becomes the serial updating rule with equivariant property. This algorithm can be further simplified by using the asymptotic form of the Fisher information matrix around the equilibrium.

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Cite

Text

Yang. "Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring." Neural Information Processing Systems, 1997.

Markdown

[Yang. "Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/yang1997neurips-multiplicative/)

BibTeX

@inproceedings{yang1997neurips-multiplicative,
  title     = {{Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring}},
  author    = {Yang, Howard Hua},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {696-702},
  url       = {https://mlanthology.org/neurips/1997/yang1997neurips-multiplicative/}
}