The PARAFAC2 decomposition is regarded as a promising multi-linear signal processing
tool in a variety of scientific fields. Designing efficient computation algorithms for PARAFAC2
is a long-standing topic. In this contribution, we propose to incorporate a dimension
reduction in direct fitting-based schemes for the computation of PARAFAC2. Extensive
simulations show that the proposed algorithms are able to achieve the same performance in
terms of the residual as their counterpart schemes in the literature, while requiring a …

The PARAFAC2 decomposition is a generalization of the PARAFAC/CP (Canonical
Polyadic) tensor decomposition. This tensor decomposition represents a set of coupled
matrix decompositions with one mode in common, ie, one of the components varies along
the set of matrices (tensor slices), whereas the second component stays constant. It has
many applications including the analysis of chromatographic data, data clustering, and
biomedical signal processing. Typically, the PARAFAC2 decomposition is described using a …