To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however well known that least squares techniques s...

The high-performance liquid chromatography-infrared spectroscopy (HPLC-IR) technique utilizing on-line flow through cell (FTC) detection has an inherent practical problem: strong absorption bands of the eluent may mask valuable analytical regions of the IR spectrum. The experimentalists' answer to this challenge is physical elimination of the chromatographic eluent before spectroscopic detectio...

In several signal processing applications for wireless communications, the received signal is multidimensional in nature and may exhibit a multilinear algebraic structure. In this context, the PARAFAC tensor decomposition has been the subject of several works in the past six years. However, generalized tensor decompositions are necessary for covering a wider class of wireless communication syst...

In this paper, we present an overview of constrained parallel factor (PARAFAC) models where the constraints model linear dependencies among columns of the factor matrices of the tensor decomposition or, alternatively, the pattern of interactions between different modes of the tensor which are captured by the equivalent core tensor. Some tensor prerequisites with a particular emphasis on mode co...

The main aim of this study was the application of excitation-emission fluorescence matrices (EEMs) combined with two decomposition methods: parallel factor analysis (PARAFAC) and nonnegative matrix factorization (NMF) to study the interaction mechanisms between humic acids (HAs) and Zn(II) over a wide concentration range (0-50 mg·dm-3). The influence of HA properties on Zn(II) complexation was ...

Higher-order tensor analysis is a multi-disciplinary tool widely used in numerous application areas involving data analysis such as psychometrics, chemometrics, and signal processing, just to mention a few. The parallel factor (PARAFAC) decomposition, also known by the acronym CP (standing for “CANDECOMP/PARAFAC” or yet “canonical polyadic”) is the most popular tensor decomposition. Its widespr...

In hope of developing a method for oil spill detection in laser remote sensing, a series of refined and crude oil samples were investigated using time-resolved fluorescence in conjunction with parallel factors analysis (PARAFAC). The time resolved emission spectra of those investigated samples were taken by a laser remote sensing system on a laboratory basis with a detection distance of 5 m. Ba...

Understanding how the concentration and chemical quality of dissolved organic matter (DOM) varies in soils is critical because DOM inXuences an array of biological, chemical, and physical processes. We used PARAFAC modeling of excitation–emission Xuorescence spectroscopy, speciWc UV absorbance (SUVA254) and biodegradable dissolved organic carbon (BDOC) incubations to investigate the chemical qu...

Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for support vector machine (SVM). The multi-scale analysis studied to reconstruct tensor feature information based on three-dimensional matrix time, frequency, spatial vectors. A wavelet used transfo...