Istituto di Scienza e Tecnologie dell'Informazione     
Salerno E. A noisy data model for MaxNG. Internal note, 2006.
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.
Subject Noisy linear mixture models
Gaussian noise
Nonparametric estimation
I.6.5 Model development

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