A combination of parallel factor analysis (PARAFAC) and principal component analysis (PCA) was used to parameterize the articulatory pattern of tongue, jaw and lip movements in 8 English vowels produced by 7 subjects with amyotrophic lateral sclerosis (ALS). A two-factor PARAFAC model derived an overall articulatory pattern represented by two basic modes dominated by tongue raising and advancem...

There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as ge...

MOTIVATION The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization. SUMMARY With a goal of developing an automated and robust way of ...

The fluorescence intensity of dissolved organic matter (DOM) in aqueous samples is known to be highly influenced by temperature. Although several studies have demonstrated the effect of thermal quenching on the fluorescence of DOM, no research has been undertaken to assess the effects of temperature by combining fluorescence excitation - emission matrices (EEM) and parallel factor analysis (PAR...

Analysis of neural data with multiple modes and high density has recently become a trend with the advances in neuroscience research and practices. There exists a pressing need for an approach to accurately and uniquely capture the features without loss or destruction of the interactions amongst the modes (typically) of space, time, and frequency. Moreover, the approach must be able to quickly a...

We study the decomposition of a nonnegative tensor into a minimal sum of outer product of nonnegative vectors and the associated parsimonious näıve Bayes probabilistic model. We show that the corresponding approximation problem, which is central to nonnegative parafac, will always have optimal solutions. The result holds for any choice of norms and, under a mild assumption, even Brègman diverge...

Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl-, SO4(2-), SiO2, Ca2+, Mg2+, Na+, K+, hardness, NO3(-), NO2(-), NH4(+), COD, PO4(3-), Zn2+, Cu2+, Mn...

Parallel Factor Analysis (PARAFAC) is used in many scientific disciplines to decompose multidimensional datasets into principal factors in order to uncover relationships in the data. While quite popular, the common implementations of PARAFAC are single server solutions that do not scale well to very large datasets. To address this limitation, a Parallel PARAFAC algorithm has been designed and i...

An improved trilinear decomposition algorithm based on a Lagrange operator (LO) is developed in this paper, which introduces a Lagrange operator and penalty terms in the loss function to improve the performance of the algorithm. Compared to the traditional parallel factor (PARAFAC) algorithm, the algorithm not only may converge much faster, but also overcome the sensibility to estimate the numb...

Alpha-divergence-based nonnegative tensor factorization (NTF) is applied to blind multispectral image (MSI) decomposition. The matrix of spectral profiles and the matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models. NTF preserves local structure in the MSI that is lost as a result of vectorization of the image when...