Numerical methods for tensor decompositions. From least squares to beta-divergence, from batch to updating. KU Leuven
Over the years, many algorithms have been designed to decompose a matrix into a product of other matrices. These matrix decompositions can be used to compress data with a minimal loss of information or for extracting meaningful components. More recently, tensor decompositions such as the canonical
polyadic decomposition (CPD) and the low multilinear rank approximation (LMLRA) have been designed as higher-order generalizations of these ...