| Full text | |
| Author(s): |
Fantinato, Denis G.
;
Duarte, Leonardo T.
;
Zanini, Paolo
;
Rivet, Bertrand
;
Attux, Romis
;
Jutten, Christian
;
Tichavsky, P
;
BabaieZadeh, M
;
Michel, OJJ
;
ThirionMoreau, N
Total Authors: 10
|
| Document type: | Journal article |
| Source: | LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017); v. 10169, p. 10-pg., 2017-01-01. |
| Abstract | |
In the context of Post-Nonlinear (PNL) mixtures, source separation can be performed in a two-stage approach, which encompasses a nonlinear and a linear compensation part. In the former part, it is usually required the knowledge of all the source distributions. In this work, we propose a less restrictive approach, where only one source distribution is needed to be known-here, chosen to be a colored Gaussian. The other sources are only required to present a time structure. The method combines, in a joint-based approach, the use of the second-order statistics (SOS) and the matching of distributions, which shows to be less costly than the classical method of computing the marginal entropy for all sources. The simulation results are favorable to the proposal. (AU) | |
| FAPESP's process: | 15/23424-6 - Nonlinear Blind Source Separation for Statistically Dependent Sources |
| Grantee: | Denis Gustavo Fantinato |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 13/14185-2 - New Methods for Adaptive Equalization Based on Information Theoretic Learning |
| Grantee: | Denis Gustavo Fantinato |
| Support Opportunities: | Scholarships in Brazil - Doctorate |