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Matrix and Tensor Decompositions in Signal Processing, Volume 2 by Gérard Favier (2021, Hardcover)

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Matrix and Tensor Decompositions in Signal Processing, Hardcover by Favier, Gérard, ISBN 1786301555, ISBN-13 9781786301550, Brand New, Free shipping in the US The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.

About this product

Product Identifiers

PublisherWiley & Sons, Incorporated, John

ISBN-101786301555

ISBN-139781786301550

eBay Product ID (ePID)4038730568

Product Key Features

Number of Pages384 Pages

Publication NameMatrix and Tensor Decompositions in Signal Processing, Volume 2

LanguageEnglish

Publication Year2021

SubjectProgramming / Algorithms, Signals & Signal Processing, Calculus, Matrices

TypeTextbook

AuthorGérard Favier

Subject AreaMathematics, Computers, Technology & Engineering

FormatHardcover

Dimensions

Item Height0.4 in

Item Weight16 Oz

Item Length0.4 in

Item Width0.4 in

Additional Product Features

Intended AudienceScholarly & Professional

Dewey Edition23

IllustratedYes

Dewey Decimal621.38220151

Table Of ContentVolume 2 1. Matrix decompositions 2. Tensor decompositions 3. Tensor networks 4. Parametric estimation of tensor decompositions 5. Recovery of low rank matrix reconnects (LRMR) and low-tensor recovery (LRTR)

SynopsisThe second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.

LC Classification NumberTK5102.9