Paper data
Title:
Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging.
Author(s):
Cardoso Jean-Francois, CNRS / ENST - TSI, Paris France
Snoussi Hichem, L2S - Supelec, Gif-sur-Yvette, France
Delabrouille Jacques, PCC - College de France, Paris, France
Patanchon Guillaume, PCC - College de France, Paris, France
Page numbers in the proceedings:
Volume I pp 561-564
Session:
Blind Source Separation / Independent Component Analysis (1/2)
Blind separation of noisy Gaussian stationary sources. Application to cosmic microwave background imaging.
Author(s):
Cardoso Jean-Francois, CNRS / ENST - TSI, Paris France
Snoussi Hichem, L2S - Supelec, Gif-sur-Yvette, France
Delabrouille Jacques, PCC - College de France, Paris, France
Patanchon Guillaume, PCC - College de France, Paris, France
Page numbers in the proceedings:
Volume I pp 561-564
Session:
Blind Source Separation / Independent Component Analysis (1/2)
Paper abstract
We present a new source separation method which maximizes the likelihood of a model of noisy mixtures of stationary, possibly Gaussian, independent components. The method has been devised to address an astronomical imaging problem. It works in the spectral domain where, thanks to two simple approximations, the likelihood assumes a simple form which is easy to handle (low dimensional sufficient statistics) and to maximize (via the EM algorithm).
Paper
