6 accepted papers
The Canonical Polyadic Decomposition (CPD) is a fundamental tensor decomposition which has widespread use in signal processing due to its ability to extract component information. A popular for algorithm CPD is the generalized eigenvalue decomposition (GEVD) which is based on the generalized eigenve…
The Canonical Polyadic Decomposition (CPD) is one of the most basic tensor models used in signal processing and machine learning. Despite its wide applicability, identifiability conditions and algorithms for CPD in cases where the tensor is incomplete are lagging behind its practical use. We first p…
Tensor updating methods enable tensor decompositions to adapt quickly when new data is added to the tensor. At present, updating methods for the canonical polyadic decomposition (CPD) give every tensor entry the same weight. In practice, however, data quality or relative importance might differ betw…
Independent component analysis (ICA) research has been driven by various applications in biomedical signal separation, telecommunications, speech analysis, and more. One particular class of algorithms for instantaneous ICA uses tensors, which have useful properties. In an attempt to port these prope…
Coupled decompositions of multiple tensors are fundamental tools for multi-set data fusion. In this paper, we introduce a coupled version of the rank-(L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</inf> , L <inf xmlns:mml="http://www.w3.org/1998/M…
A novel deterministic blind signal separation technique for separating signals into rational functions is proposed, applicable in various situations. This new technique is based on a tensorization of the observed data matrix into a set of Löwner matrices. The obtained tensor can then be decomposed w…