Salerno E.
Noisy linear mixture models Gaussian noise Nonparametric estimation
A noise-insensitive Euclidean distance function is derived for the MaxNG algorithm. This is a dependent-component-analysis source-separation algorithm based on the maximization of a nongaussianity measure, and has recently been developed for a noiseless mixture model. It is shown that, in the case of observations corrupted by signal-independent stationary Gaussian noise, the probability density function of the output process can be easily made independent of noise if it is approximated via the Parzen-windows method with Gaussian kernels. The role assumed by the aperture parameter is shown to be similar to the one of the regularization parameter in any inverse problem.
Source: ISTI Technical reports, 2006
@techreport{oai:it.cnr:prodotti:160398, title = {A noisy data model for MaxNG}, author = {Salerno E.}, institution = {ISTI Technical reports, 2006}, year = {2006} }