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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cardoso, J.-F. |
| Copyright Year | 1998 |
| Description | Author affiliation: ENST TSI, CNRS, Paris, France (Cardoso, J.-F.) |
| Abstract | The blind signal separation (BSS) problem has a distinctive feature: the unknown parameter being an invertible matrix, the parameter set is a multiplicative group and the observations can be modeled by a transformation model. For this reason, it is possible to design on-line algorithms which are very simple and still offer excellent performance (typically: Newton-like performance at a gradient-like cost). This paper presents two apparently different approaches to deriving these algorithms from the maximum likelihood principle. One approach (relative gradient) starts with a focus on the group structure and eventually introduces the statistical structure. The other approach (natural gradient) applies to any statistical manifold and is eventually made tractable by exploiting the group structure. The relationship between these approaches is explained. |
| Starting Page | 136 |
| Ending Page | 139 |
| File Size | 376759 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780350103 |
| DOI | 10.1109/SSAP.1998.739353 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-09-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer aided software engineering Source separation Maximum likelihood estimation Vectors Adaptive algorithm Blind source separation Independent component analysis Equations Algorithm design and analysis Costs |
| Content Type | Text |
| Resource Type | Article |
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