Subspace identification through blind source separation
2006
Article
ei
Given a linear and instantaneous mixture model, we prove that for blind source separation (BSS) algorithms based on mutual information, only sources with non-Gaussian distribution are consistently reconstructed independent of initial conditions. This allows the identification of non-Gaussian sources and consequently the identification of signal and noise subspaces through BSS. The results are illustrated with a simple example, and the implications for a variety of signal processing applications, such as denoising and model identification, are discussed.
Author(s): | Grosse-Wentrup, M. and Buss, M. |
Journal: | IEEE Signal Processing Letters |
Volume: | 13 |
Number (issue): | 2 |
Pages: | 100-103 |
Year: | 2006 |
Month: | February |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1109/LSP.2005.861581 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @article{4979, title = {Subspace identification through blind source separation}, author = {Grosse-Wentrup, M. and Buss, M.}, journal = {IEEE Signal Processing Letters}, volume = {13}, number = {2}, pages = {100-103}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = feb, year = {2006}, doi = {10.1109/LSP.2005.861581}, month_numeric = {2} } |