Independent Component Analysis (ICA) and Method of Estimating Functions
Summary :
Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.3 pp.540-547
- Publication Date
- 2002/03/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on the Trend of Digital Signal Processing and Its Future Direction)
- Category
- Theories
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Keyword
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Shun-ichi AMARI, "Independent Component Analysis (ICA) and Method of Estimating Functions" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 3, pp. 540-547, March 2002, doi: .
Abstract: Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_3_540/_p
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@ARTICLE{e85-a_3_540,
author={Shun-ichi AMARI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Independent Component Analysis (ICA) and Method of Estimating Functions},
year={2002},
volume={E85-A},
number={3},
pages={540-547},
abstract={Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Independent Component Analysis (ICA) and Method of Estimating Functions
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 540
EP - 547
AU - Shun-ichi AMARI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2002
AB - Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.
ER -
English
