Compute the canonical polyadic decomposition, multilinear singular value decomposition, block term decompositions and low multilinear rank approximation.
Structured data fusion
Define your own (coupled) matrix and tensor factorizations with structured factors and support for dense, sparse, incomplete and structured data sets.
Quasi-Newton and nonlinear least squares optimization with complex variables including numerical complex differentiation.
Global minimization of bivariate polynomials & rational functions
Real and complex exact line search (LS) and real exact plane search (PS) for tensor optimization.
Structured tensor representations
Obtain faster tensor operations and decompositions by exploiting the structure of the data, such as Hankel, Tensor Train and CPD structure.
And much more
Tensorize data, compute higher-order statistics, visualize tensors of arbitrary order, estimate a tensor's rank or multilinear rank, ...
Basic Use of Tensorlab
Learn the basics of Tensorlab. This demo explains how to construct and visualize tensors. It also explains how to compute a CPD.
Multidimensional Harmonic Retrieval
The goal of this demo is to illustrate the use of the basic cpd command in an array processing application.
Independent Component Analysis
Independent component analysis finds latent variables that are statistically independent in observed data. The two related demos illustrate the computation of basic as well as constrained CPD.
Independent Vector Analysis
Independent vector analysis is a multi-set extension of independent component analysis. The problem is solved as an example of structured data fusion.
GPS: Predicting User Involvement
The goal of this demo is to predict whether a user participates in a certain activity in a certain place using a real life GPS dataset. This demo illustrates how to use the structured data fusion framework.