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