Blind Source Separation for Text Mining

Blind wideband source separation

This paper deals with the general problem of separating two independent wideband sources when they are mixed by unknown filters. In order to solve this problem. a backward framework is proposed which is composed of two different stages. The fiist one consists of two linear predictors devoted to improve the source separation, whitening the input signals. Their coefficients are calculated applyin...

Blind Equalization via Blind Source Separation Techniques

This paper addresses the application of blind source separation (BSS) algorithms to the problem of blind equalization (BE) in a frequency selective multiuser environment. Compared with other algorithms that exploit the structural properties of the channel or data matrix, our approaches are insensitive to channel order estimation, robust to ill-conditioned channels and able to choose the \best" ...

Blind and semi-blind source separation

MISEP Method for Postnonlinear Blind Source Separation

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network ...

Generalized Mixture Models for Blind Source Separation

Neural Independent Component Analysis (ICA) algorithms based on unimodal source distributions provide acceptable performances in the case of Blind Source Separation (BSS) of super-gaussian sources. However, their convergence profiles are significantly slower in the case of sub-gaussian sources. In some situations it is necessary to deal with sub-gaussian signals in the form of noise or others. ...