Matrix and Tensor Decompositions in Signal Processing Volume 2

This book PDF is perfect for those who love Technology & Engineering genre, written by Gérard Favier and published by John Wiley & Sons which was released on 31 August 2021 with total hardcover pages 386. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Matrix and Tensor Decompositions in Signal Processing Volume 2 books below.

Matrix and Tensor Decompositions in Signal Processing  Volume 2
Author : Gérard Favier
File Size : 48,6 Mb
Publisher : John Wiley & Sons
Language : English
Release Date : 31 August 2021
ISBN : 9781786301550
Pages : 386 pages
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Matrix and Tensor Decompositions in Signal Processing Volume 2 by Gérard Favier Book PDF Summary

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.

Matrix and Tensor Decompositions in Signal Processing  Volume 2

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

Get Book
Matrix and Tensor Decompositions in Signal Processing  Volume 2

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

Get Book
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