Matrix and Tensor Decomposition
Application to Data Fusion and Analysis
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Product details:
- Publisher Academic Press
- Date of Publication 1 February 2024
- ISBN 9780128157602
- Binding Paperback
- No. of pages400 pages
- Size 235x191 mm
- Language English 0
Categories
Long description:
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
Table of Contents:
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
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