Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …

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

Human brains lie at the core of complex neurobiological systems, where the neurons,
circuits, and subsystems interact in enigmatic ways. Understanding the structural and …

Micro-expressions are facial expressions which are fleeting and reveal genuine emotions
that people try to conceal. These are important clues for detecting lies and dangerous …

Kinship verification from facial images is an interesting and challenging problem in computer
vision, and there are very limited attempts on tackle this problem in the literature. In this …

In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …

Conventional appearance-based face recognition methods usually assume that there are
multiple samples per person (MSPP) available for discriminative feature extraction during …