جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://dl.pgu.ac.ir/handle/Hannan/81497
Title: Tensor decompositions for signal processing applications: from two-way to multiway component analysis
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; Electrical And Electronic Engineering; Mechanical Engineering
Issue Date: 16-Jan-2017
10-Feb-2015
1-Feb-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Description: 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.
Other Identifiers: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349771400016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
1053-5888
http://hdl.handle.net/10044/1/43621
https://dx.doi.org/10.1109/MSP.2013.2297439
Type Of Material: Other
Appears in Collections:Faculty of Engineering

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