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Fast Algorithms For Bayesian Independent Component Analysis (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Pajunen, Petteri Lappalainen, Harri |
| Description | In Proceedings of the Second International Workshop on Independent Component Analysis and Signal Separation (ICA2000 |
| Abstract | Fast algorithms for linear blind source separation are developed. The fast convergence is first derived from low-noise approximation of the EM-algorithm given in [1], to which a modifcation is made that leads as a special case to the FastICA algorithm [2]. The modifcation is given a general interpretation and is applied to Bayesian blind source separation of noisy signals. |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | General Interpretation Low-noise Approximation Fast Algorithm Fast Convergence Linear Blind Source Separation Bayesian Blind Source Separation Fastica Algorithm Bayesian Independent Component Analysis Noisy Signal |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings |
