235,52 €
261,69 €
-10% with code: EXTRA
Matrix and Tensor Decompositions in Signal Processing
Matrix and Tensor Decompositions in Signal Processing
235,52
261,69 €
  • We will send in 10–14 business days.
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 ran…
261.69
  • Publisher:
  • Year: 2020
  • Pages: 200
  • ISBN-10: 1786301555
  • ISBN-13: 9781786301550
  • Format: 15.6 x 23.4 x 2.2 cm, kieti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

Matrix and Tensor Decompositions in Signal Processing (e-book) (used book) | bookbook.eu

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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.

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  • Author: Gerard Favier
  • Publisher:
  • Year: 2020
  • Pages: 200
  • ISBN-10: 1786301555
  • ISBN-13: 9781786301550
  • Format: 15.6 x 23.4 x 2.2 cm, kieti viršeliai
  • Language: English English

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.

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