Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
| Hi-index | 0.08 |
Two major approaches for blind source separation (BSS) are, respectively, based on the maximum likelihood (ML) principle and mutual information (MI) minimization. They have been mainly studied for simple linear mixtures. We here show that they additionally involve indirect functional dependencies for general nonlinear mixtures. Moreover, the notations commonly employed by the BSS community in calculations performed for these methods may become misleading when using them for nonlinear mixtures, due to the above-mentioned dependencies. In this paper, we first explain this phenomenon for arbitrary nonlinear mixing models. We then accordingly correct two previously published methods for specific nonlinear mixtures, where indirect dependencies were mistakenly ignored. This paper therefore opens the way to the application of the ML and MI BSS methods to many specific mixing models, by providing general tools to address such mixtures and explicitly showing how to apply these tools to practical cases.