Tensor decompositions are at the core of many blind source separation (BSS) algorithms,
either explicitly or implicitly. In particular, the canonical polyadic (CP) tensor decomposition …

We introduce an inductive method for the study of the uniqueness of decompositions of
tensors, by means of tensors of rank 1. The method is based on the geometric notion of …

One of the most popular, and versatile, ways of visually analyzing the associating between
categorical data is to perform a correspondence analysis on the contingency table that is …

Canonical polyadic decomposition (CPD) of a higher-order tensor is decomposition into a
minimal number of rank-1 tensors. We give an overview of existing results concerning …

We study a class of orbit recovery problems in which we observe independent copies of an
unknown element of R p, each linearly acted upon by a random element of some group …

We propose a new sufficient condition for verifying whether general rank-r complex tensors
of arbitrary order admit a unique decomposition as a linear combination of rank-1 tensors. A …

We discuss how recently discovered techniques and tools from compressed sensing can be
used in tensor decompositions, with a view towards modeling signals from multiple arrays of …

Latent class models have wide applications in social and biological sciences. In many
applications, prespecified restrictions are imposed on the parameter space of latent class …

Restricted latent class models (RLCMs) have recently gained prominence in educational
assessment, psychiatric evaluation, and medical diagnosis. In contrast to conventional latent …

The rank of a tensor is analysed in the context of quantum entanglement. A pure quantum
state v of a composite system consisting of d subsystems with n levels each is viewed as a …