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Matrix and Tensor Decompositions in Signal Processing
Matrix and Tensor Decompositions in Signal Processing
Knygos.lt klubas Knygos.lt nariams
182,27 €
-30%
Įprastai
260,39 €
  • Išsiųsime per 12–18 d.d.
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…
  • Leidėjas:
  • Metai: 2020
  • Puslapiai: 200
  • ISBN-10: 1786301555
  • ISBN-13: 9781786301550
  • Formatas: 15.6 x 23.4 x 2.2 cm, kieti viršeliai
  • Kalba: Anglų

Matrix and Tensor Decompositions in Signal Processing (el. knyga) (skaityta knyga) | knygos.lt

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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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  • Autorius: Gerard Favier
  • Leidėjas:
  • Metai: 2020
  • Puslapiai: 200
  • ISBN-10: 1786301555
  • ISBN-13: 9781786301550
  • Formatas: 15.6 x 23.4 x 2.2 cm, kieti viršeliai
  • Kalba: Anglų

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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