Independent component analysis is a probabilistic method for learning a linear transform of
a random vector. The goal is to find components that are maximally independent and non …

The majority of data sets collected by researchers in all disciplines are multivariate, meaning
that several measurements, observations, or recordings are taken on each of the units in the …

A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …

Independent component analysis (ICA) is a statistical method for transforming an observed
multidimensional random vector into components that are statistically as independent from …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

This tutorial text provides the reader with an understanding of artificial neural networks
(ANNs), and their application, beginning with the biological systems which inspired them …

This work presents a novel method for learning a model that can diagnose Attention Deficit
Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional …

This paper compares principal component analysis (PCA) and independent component
analysis (ICA) in the context of a baseline face recognition system, a comparison motivated …

High-frequency electroencephalography (EEG) signals play an important role in research on
human emotions. However, the different network patterns under different emotional states in …

We derive an exact solution to the problem of maximum likelihood estimation of the
supervector covariance matrix used in extended MAP (or EMAP) speaker adaptation and …