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.
By:
Gérard Favier (CNRS) Imprint: ISTE Ltd and John Wiley & Sons Inc Country of Publication: United Kingdom Edition: Volume 2 Dimensions:
Height: 10mm,
Width: 10mm,
Weight: 454g ISBN:9781786301550 ISBN 10: 1786301555 Pages: 384 Publication Date:10 August 2021 Audience:
Professional and scholarly
,
Undergraduate
Format:Hardback Publisher's Status: Active
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)
FAVIER Gérard, Emeritus Research Director at CNRS.