Big data is a potential research area receiving considerable attention from academia and IT
communities. In the digital world, the amounts of data generated and stored have expanded …

Efforts to raise the bar of higher education so as to respond to dynamic societal/industry
needs have led to a number of initiatives, including artificial neural network (ANN) based …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Tensors or multiway arrays are functions of three or more indices (i, j, k,...)-similar to matrices
(two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a …

Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone
of traditional machine learning and data analysis, tensor methods have been gaining …

The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move …

Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …