Koopman mode decomposition and tensor component analysis (also known as CANDECOMP/PARAFAC or canonical polyadic decomposition) are two popular approaches of decomposing high dimensional data sets into low modes that capture the most relevant features and/or dynamics. Despite their similar goal, methods largely used by different scientific communities formulated in distinct mathematical language...

The increasing use of multiple sensors, which produce a large amount multi-dimensional data, requires efficient representation and classification methods. In this paper, we present new method for data that relies on two premises: (1) are usually represented by tensors, since brings benefits from multilinear algebra established tensor factorization methods; (2) can be described subspace vector s...

This study deals with the missing answers prediction problem. We address this problem using coupled analysis of ImageCLEF2014 dataset by representing it as a heterogeneous data, i.e., dataset in the form of matrices. We propose to use an approach based on probabilistic interpretation of tensor factorization models, i.e., Generalized Coupled Tensor Factorization, which can simultaneously fit a l...

We examine the problem of the construction of a first order symmetric hyperbolic evolution system for the Einstein-Maxwell-Euler system. Our analysis is based on a 1 + 3 tetrad formalism which makes use of the components of the Weyl tensor as one of the unknowns. In order to ensure the symmetric hyperbolicity of the evolution equations implied by the Bianchi identity, we introduce a tensor of r...