| Full text | |
| Author(s): |
Fantinato, Denis G.
;
Duarte, Leonardo T.
;
Rivet, Bertrand
;
Ehsandoust, Bahram
;
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 this work, we consider the nonlinear Blind Source Separation (BSS) problem in the context of overdetermined Bilinear Mixtures, in which a linear structure can be employed for performing separation. Based on the Gaussian Process (GP) framework, two approaches are proposed: the predictive distribution and the maximization of the marginal likelihood. In both cases, separation can be achieved by assuming that the sources are Gaussian and temporally correlated. The results with synthetic data 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 |