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Matrix and Tensor Decomposition: Application to Data Fusion and Analysis

Autor Christian Jutten, Dana Lahat, Tulay Adali
en Limba Engleză Paperback – feb 2024
Matrix and Tensor Decomposition: Application to Data Fusion and Analysis introduces the main theoretical concepts for data fusion using matrix and tensor decompositions, beginning with the concept of "diversity", which facilitates identifiability. It provides the link between theoretical results and practice by addressing key implementation issues, such as model choice for a given problem, identification of sources of diversity, parameter selection and performance evaluation. Using rich diagrams to help communicate the main ideas and relationships among models and methods, this book presents a readily accessible reference for researchers on the methods and application of matrix and tensor decompositions.


  • Introduces basic theory and practice of data fusion, along with the concept of "diversity" as a key concept for interpretability and identifiability of a given decomposition
  • Provides a unifying framework for basic matrix and tensor decompositions, considering both algebraic and statistical points-of-view and discussing their relationships
  • Addresses key questions in implementation, most importantly, that of model order selection and other parameters
  • Provides tools for model order selection so that the signal subspace can be identified
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Specificații

ISBN-13: 9780128157602
ISBN-10: 0128157607
Pagini: 400
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Public țintă

Researchers and graduate students in electronic engineering computer scientists, medical imaging and applied mathematics

Cuprins

1. Introduction 2. ICA and IVA: A Bottom-up Approach 3. ICA and IVA: A Top-down Approach 4. Sparse Decompositions 5. Nonnegative Decompositions 6. Tensor Decompositions 7. Data Fusion and Analysis Through 8. Data Fusion and Analysis Using General 9. Implementation Issues and Open