Abstract
In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method.
Original language | English |
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Pages (from-to) | 2149-2157 |
Number of pages | 9 |
Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
Volume | E89-A |
Issue number | 8 |
DOIs | |
Publication status | Published - 2006 Aug |
Externally published | Yes |
Keywords
- Characteristic function
- Contrast function
- Independent component analysis
- Non-Gaussianity measure
ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics