A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.8 pp.2149-2157
- Publication Date
- 2006/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.8.2149
- Type of Manuscript
- Special Section PAPER (Special Section on Papers Selected from the 20th Symposium on Signal Processing)
- Category
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Muhammad TUFAIL, Masahide ABE, Masayuki KAWAMATA, "A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 8, pp. 2149-2157, August 2006, doi: 10.1093/ietfec/e89-a.8.2149.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.8.2149/_p
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@ARTICLE{e89-a_8_2149,
author={Muhammad TUFAIL, Masahide ABE, Masayuki KAWAMATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals},
year={2006},
volume={E89-A},
number={8},
pages={2149-2157},
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.},
keywords={},
doi={10.1093/ietfec/e89-a.8.2149},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2149
EP - 2157
AU - Muhammad TUFAIL
AU - Masahide ABE
AU - Masayuki KAWAMATA
PY - 2006
DO - 10.1093/ietfec/e89-a.8.2149
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2006
AB - 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.
ER -
English
