Matrix and Tensor Decomposition: Application to Data Fusion and Analysis

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.