In applied research data are often collected from sources with a high dimensional
multivariate output. Analysis of such data is composed of eg extraction and characterization …

In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF
problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not …

Current high-throughput data acquisition technologies probe dynamical systems with
different imaging modalities, generating massive data sets at different spatial and temporal …

Parallel factor analysis (PARAFAC) is a powerful tool for detecting latent components in
human electroencephalogram (EEG) in the time-space-frequency domain. As an essential …

Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool
for neuronal signal decoding and brain–computer interface (BCI) system calibration. This …

Nonnegative (linear) least square problems are a fundamental class of problems that is well-
studied in statistical learning and for which solvers have been implemented in many of the …

Significance Current techniques for data analysis in functional near-infrared spectroscopy
(fNIRS), such as artifact correction, do not allow to integrate the information originating from …

The canonical polyadic (CP) tensor decomposition decomposes a multidimensional data
array into a sum of outer products of finite-dimensional vectors. Instead, we can replace …

The goal of this study was to capture the electroencephalographic signature of
experimentally induced pain and pain-modulating mechanisms after painful peripheral …

Component models such as PCA and ICA are often used to reduce neuroimaging data into
a smaller number of components, which are thought to reflect latent brain networks. When …