Open AccessSequential Sparse Blind Source Separation for Non-Linear Mixtures
Author(s) -
Christophe Kervazo,
J. Bobin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1476/1/012008
Subject(s) - blind signal separation , computer science , separation (statistics) , mixing (physics) , linear approximation , algorithm , inverse problem , linear map , source separation , mathematics , nonlinear system , machine learning , physics , mathematical analysis , quantum mechanics , pure mathematics , computer network , channel (broadcasting)
Linear Blind Source Separation (BSS) has known a tremendous success in fields ranging from biomedical imaging to astrophysics. In this work, we however propose to depart from the usual linear setting and tackle the case in which the sources are mixed by an unknown non-linear function. We propose to use a sequential decomposition of the data enabling its approximation by a linear-by-part function. Beyond separating the sources, the introduced StackedAMCA can further empirically learn in some settings an approximation of the inverse of the unknown non-linear mixing, enabling to reconstruct the sources despite a severely ill- posed problem. The quality of the method is demonstrated experimentally, and a comparison is performed with state-of-the art non-linear BSS algorithms.