A1 Refereed original research article in a scientific journal
Independent component analysis for tensor-valued data
Authors: Virta J, Li B, Nordhausen K, Oja H
Publisher: ELSEVIER INC
Publication year: 2017
Journal: Journal of Multivariate Analysis
Journal name in source: JOURNAL OF MULTIVARIATE ANALYSIS
Journal acronym: J MULTIVARIATE ANAL
Volume: 162
First page : 172
Last page: 192
Number of pages: 21
ISSN: 0047-259X
DOI: https://doi.org/10.1016/j.jmva.2017.09.008
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/27390341
Abstract
In preprocessing tensor-valued data, e.g., images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to one of the many methods used for independent component analysis (ICA). However, the tensor structure of the original data is lost in the vectorization and, as a more suitable alternative, we propose the matrix- and tensor fourth order blind identification (MFOBI and TFOBI). In these tensorial extensions of the classic fourth order blind identification (FOBI) we assume a Kronecker structure for the mixing and perform FOBI simultaneously on each direction of the observed tensors. We discuss the theory and assumptions behind MFOBI and TFOBI and provide two different algorithms and related estimates of the unmixing matrices along with their asymptotic properties. Finally, simulations are used to compare the method's performance with that of classical FOBI for vectorized data and we end with a real data clustering example. (C) 2017 Elsevier Inc. All rights reserved.
In preprocessing tensor-valued data, e.g., images and videos, a common procedure is to vectorize the observations and subject the resulting vectors to one of the many methods used for independent component analysis (ICA). However, the tensor structure of the original data is lost in the vectorization and, as a more suitable alternative, we propose the matrix- and tensor fourth order blind identification (MFOBI and TFOBI). In these tensorial extensions of the classic fourth order blind identification (FOBI) we assume a Kronecker structure for the mixing and perform FOBI simultaneously on each direction of the observed tensors. We discuss the theory and assumptions behind MFOBI and TFOBI and provide two different algorithms and related estimates of the unmixing matrices along with their asymptotic properties. Finally, simulations are used to compare the method's performance with that of classical FOBI for vectorized data and we end with a real data clustering example. (C) 2017 Elsevier Inc. All rights reserved.
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