IEICE TRANSACTIONS on Fundamentals
Quadratic Independent Component Analysis
Summary :
The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.9 pp.2355-2363
- Publication Date
- 2004/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Fabian J. THEIS, Wakako NAKAMURA, "Quadratic Independent Component Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 9, pp. 2355-2363, September 2004, doi: .
Abstract: The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_9_2355/_p
Copy
@ARTICLE{e87-a_9_2355,
author={Fabian J. THEIS, Wakako NAKAMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Quadratic Independent Component Analysis},
year={2004},
volume={E87-A},
number={9},
pages={2355-2363},
abstract={The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Quadratic Independent Component Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2355
EP - 2363
AU - Fabian J. THEIS
AU - Wakako NAKAMURA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2004
AB - The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.
ER -
English
