Tensor decompositions for signal processing applications: from two-way to multiway component analysis
File(s)
Accepted version
Author(s)
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.
Date Issued
2015-03-01
Date Acceptance
2015-02-01
Citation
IEEE: Signal Processing Magazine, 2015, 32 (2), pp.145-163
ISSN
1053-5888
Publisher
Institute of Electrical and Electronics Engineers
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.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349771400016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
CANONICAL POLYADIC DECOMPOSITION
ALTERNATING LEAST-SQUARES
BLIND SOURCE SEPARATION
RANK APPROXIMATION
L-R
ALGORITHMS
UNIQUENESS
CANDECOMP/PARAFAC
FACTORIZATIONS
OPTIMIZATION
Networking & Telecommunications
0801 Artificial Intelligence and Image Processing
0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Publication Status
Published
Date Publish Online
2015-02-12