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Multidimensional independent component analysis using characteristic functions

Theis, Fabian J. (2005) Multidimensional independent component analysis using characteristic functions. In: 13. European Signal Processing Conference, EUSIPCO 2005; 4 - 8 September 2005, Antalya, Turkey; proceedings; conference CD. UNSPECIFIED.

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Abstract

The goal of multidimensional independent component analysis (MICA) lies in the linear separation of data into statistically independent groups of signals. In this work, we give an elementary proof for the uniqueness of this problem in the case of equally sized subspaces, showing that the separation matrix is essentially unique except for row permutation and scaling. The proof is based on the ...

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Item Type:Book Section
Date:2005
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
UNSPECIFIED
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:13 Oct 2010 05:54
Last Modified:13 Oct 2010 05:54
Item ID:17092
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