Semiparametric model and superefficiency in blind deconvolution

Signal Processing

Volumn 81, Issue 12, 2001, Pages 2535-2553

  Zhang, L Q ^a   Amari, S ^a   Cichocki, A ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

Blind deconvolution; Estimating function; Independent component analysis; Natural gradient; Semiparametric model; Superefficiency

Indexed keywords

INDEPENDENT COMPONENT ANALYSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS; ONLINE SYSTEMS; STATISTICAL METHODS;

BLIND DECONVOLUTION;

BLIND SOURCE SEPARATION;

EID: 0035575777     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1684(01)00146-3     Document Type: Article

References (47)

1. On subspace methods for blind identification of SIMO-FIR systems
2. Natural gradient works efficiently in learning
3. Superefficiency in blind source separation
4. Blind source separation - Semiparametric statistical approach
5. Stability analysis of adaptive blind source separation
6. Adaptive blind signal processing - Neural network approaches
7. A new learning algorithm for blind signal separation
8. Novel on-line algorithms for blind deconvolution using natural gradient approach
9. Information geometry of estimating functions in semiparametric statistical models
10. Estimating functions in semiparametric statistical models
11. Estimation in the presence of infinitely many nuisance parameters in semiparametric statistical models
12. An information maximization approach to blind separation and blind deconvolution
13. Bussgang techniques for blind deconvolution and equalization
14. Blind deconvolution vis cumulant extrema
15. Estimating equations for source separation
16. Blind signal separation: Statistical principles
17. Equivariant adaptive source separation
18. Robust learning algorithm for blind separation of signals
19. Independent component analysis: A new concept?
20. Adaptive blind separation of independent sources: A deflation approach
21. Blind identification of MIMO-FIR system: A generalized linear prediction approach
22. Fast maximum likelihood for blind identification of multiple FIR channels
23. Blind identifiability of FIR-MIMO systems with colored input using second order statistics
24. A fast fixed-point algorithm for independent component analysis
25. Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture
26. The nonlinear PCA criterion in blind source separation: Relations with other approaches
27. Techniques for adaptive equalization of digital communication systems
28. Subspace methods for the blind identification of multichannel FIR filters
29. Two extensional applications of the zero-forcing equalization method
30. Blind channel and carrier frequency offset estimation using periodic modulation precoders
31. New criteria for blind deconvolution of nonminimum phase systems (channels)
32. Indeterminacy and identifiability of blind identification
33. Multichannel blind identification: From subspace to maximum likelihood methods
34. Blind identification and equalization base on second-order statistics: A time domain approach
35. A new approach to multipath correction of constant modulus signals
36. Multistep linear predictors-based blind identification and equalization of multiple-input multiple-output channels
37. Geometrical structures of FIR manifold and their application to multichannel blind deconvolution
38. Multichannel blind deconvolution of nonminimum phase systems using information backpropagation
39. Natural gradient algorithm for blind separation of overdetermined mixture with additive noise