Name
Abstract | ||
|---|---|---|
This paper studies the problem of the simultaneous blind signal extraction of a subset of independent components from a linear mixture. In order to solve it in a robust manner, we consider the optimization of contrast functions that jointly exploit the information provided by several cumulant tensors of the observations. We develop hierarchical and simultaneous ICA extraction algorithms that are able to optimize the proposed contrast functions. These algorithms are based on the thin-QR and thin-SVD factorizations of a matrix of weighted cross-statistics between the observations and outputs. Simulations illustrate the good performance of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.5281/zenodo.38265 | EUSIPCO |
Keywords | DocType | ISBN |
higher order statistics,independent component analysis,signal processing,singular value decomposition,statistical analysis,tensors,ica extraction algorithm,blind signal extraction,cumulant tensor,linear mixture component,optimization,thin qr factorization,thin svd factorization,weighted cross-statistic matrix | Conference | 978-320-0001-65-7 |
Citations | PageRank | References |
10 | 0.67 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Cruces | 1 | 206 | 19.05 |
| Andrzej Cichocki | 2 | 5228 | 508.42 |
| De Lathauwer, L. | 3 | 107 | 10.07 |