Matrix and Tensor Decomposition Application to Data Fusion and Analysis
Auteurs : Jutten Christian, Lahat Dana, Adali Tulay
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.
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
Dana Lahat received the BSc, MSc and PhD degrees in electrical and electronics engineering from Tel Aviv University, Israel, in 1998, 2004 and 2013, respectively. She is currently a postdoctoral researcher in GIPSA-Lab, Grenoble, France. She has been awarded the Chateaubriand Fellowship of the French Government for the academic year 2007–2008. Her main research interests are statistical signal processing and source separation
Tülay Adali received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at the University of Maryland Baltimore Count
- 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
Date de parution : 02-2024
Ouvrage de 400 p.
19x23.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 97,13 €
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