Name
Abstract | ||
|---|---|---|
In this paper we present an iterative inversion (II) approach to blind source separation (BSS). It consists of a quasi-Newton method for the resolution of an estimating equation obtained from the implicit inversion of a robust estimate of the mixing system. The resulting learning rule includes several existing algorithms for BSS as particular cases giving them a novel and unified interpretation.It also provides a justification of the Cardoso and Laheld step size normalization. The II method is first presented for instantaneous mixtures and then extended to the problem of blind separation of convolutive mixtures. Finally, we derive the necessary and sufficient asymptotic stability conditions for both the instantaneous and convolutive methods to converge. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/72.883471 | IEEE Trans. Neural Netw. Learning Syst. |
Keywords | Field | DocType |
adaptive signal detection,asymptotic stability,convolution,higher order statistics,inverse problems,iterative methods,learning (artificial intelligence),neural nets,principal component analysis,adaptive signal processing,asymptotic stability,blind convolution,blind source separation,convolutive mixtures,higher order statistics,independent component analysis,iterative inversion,learning rule,quasi-Newton method | Normalization (statistics),Convolution,Iterative method,Computer science,Higher-order statistics,Learning rule,Exponential stability,Inverse problem,Artificial intelligence,Blind signal separation,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 6 | 1045-9227 |
Citations | PageRank | References |
28 | 2.13 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cruces-Alvarez, S. | 1 | 28 | 2.13 |
| A. Cichocki | 2 | 518 | 40.68 |
| Castedo-Ribas, L. | 3 | 28 | 2.13 |