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Tensor decompositions for signal processing applications: from two-way to multiway component analysis

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Title: Tensor decompositions for signal processing applications: from two-way to multiway component analysis
Authors: Cichocki, A
Mandic, DP
Anh, HP
Caiafa, CF
Zhou, G
Zhao, Q
De Lathauwer, L
Item Type: Journal Article
Abstract: The widespread use of multisensor technology and the emergence of big data sets have highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.
Issue Date: 10-Feb-2015
Date of Acceptance: 1-Feb-2015
URI: http://hdl.handle.net/10044/1/43621
DOI: https://dx.doi.org/10.1109/MSP.2013.2297439
ISSN: 1053-5888
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 145
End Page: 163
Journal / Book Title: IEEE Signal Processing Magazine
Volume: 32
Issue: 2
Copyright Statement: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
CANONICAL POLYADIC DECOMPOSITION
BLIND SOURCE SEPARATION
ALTERNATING LEAST-SQUARES
PARALLEL FACTOR-ANALYSIS
HIGHER-ORDER TENSORS
RANK APPROXIMATION
FACTOR MATRIX
1) TERMS
L-R
CANDECOMP/PARAFAC
Networking & Telecommunications
0906 Electrical And Electronic Engineering
0913 Mechanical Engineering
Publication Status: Published
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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