Description

Book Synopsis
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.

Table of Contents
Volume 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)

Matrix and Tensor Decompositions in Signal

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    £999.99

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    A Hardback by Gérard Favier

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      Publisher: ISTE Ltd and John Wiley & Sons Inc
      Publication Date: 19/11/2021
      ISBN13: 9781786301550, 978-1786301550
      ISBN10: 1786301555
      Also in:
      Digital signal processing (DSP) Electronics and communications engineering

      Description

      Book Synopsis
      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.

      Table of Contents
      Volume 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)

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