Natural Gradient Approach to Blind Separation of Over-and Under-complete Mixtures

@inproceedings{Zhang1999NaturalGA,
  title={Natural Gradient Approach to Blind Separation of Over-and Under-complete Mixtures},
  author={L.-Q. Zhang and S. Amari},
  year={1999}
}
In this paper we study natural gradient approaches to blind separation of over-and under-complete mixtures. First we introduce Lie group structures on the mani-folds of the under-and over-complete mixture matrices respectively, and endow Riemannian metrics on the manifolds based on the property of Lie groups. Then we derive the natural gradients on the manifolds using the isometry of the Riemannian metric. Using the natural gradient, we present a new learning algorithm based on the minimization… CONTINUE READING
Highly Cited
This paper has 39 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 16 references

Learning non- linear covercomplete representations for e cient coding

M. S. Lewicki, T. Sejnowski
NIPS,10, • 1998
View 5 Excerpts
Highly Influenced

Independent component analysis, A new concept?

Signal Processing • 1994
View 8 Excerpts
Highly Influenced

Natural Gradient Learning for Over- and Under-Complete Bases in ICA

Neural Computation • 1999
View 11 Excerpts
Highly Influenced

Equivariant adaptive source separation

IEEE Trans. Signal Processing • 1996
View 7 Excerpts
Highly Influenced

Blind separa- tion/deconvolution using canonical stable state- space models

L. Zhang, A. Cichocki
Proceeding of NOLTA'98, page in printing, • 1998