Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10370401 | Signal Processing | 2005 | 21 Pages |
Abstract
In this paper, a nonparametric “gradient” of the mutual information is first introduced. It is used for showing that mutual information has no local minima. Using the introduced “gradient”, two general gradient based approaches for minimizing mutual information in a parametric model are then presented. These approaches are quite general, and principally they can be used in any mutual information minimization problem. In blind source separation, these approaches provide powerful tools for separating any complicated (yet separable) mixing model. In this paper, they are used to develop algorithms for separating four separable mixing models: linear instantaneous, linear convolutive, post nonlinear (PNL) and convolutive post nonlinear (CPNL) mixtures.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Massoud Babaie-Zadeh, Christian Jutten,