Recent Numerical and Conceptual Advances for Tensor Decompositions — A Preview of Tensorlab 4.0

摘要

The fourth release of Tensorlab - a Matlab toolbox which bundles state-of-the-art tensor algorithms and tools - introduces a number of algorithms which allow a variety of new types of problems to be solved. For example, Gauss-Newton type algorithms for dealing with non-identical noise distributions or implicitly given tensors are discussed. To deal with large-scale datasets, incomplete tensors are combined with constraints, and updating techniques enable real-time tracking of time-varying tensors. A more robust algorithm for computing the decomposition in block terms is presented as well. To make tensor algorithms more accessible, graphical user interfaces for computing a decomposition in rank-1 terms or to compress a tensor are given.