Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for de-mixing coefficients into the classical I...

Independent Component Analysis (ICA) is introduced from the viewpoint of maximal information transfer for single neurons. This historical motivation for the development of ICA may be interesting from the viewpoint of independent agents because each neuron can be seen as a single agent. The current article compares the performance of ICA with Principal Component Analysis (PCA) for detecting core...

Independent Component Analysis (ICA) is a method for blind source separation of a multivariate dataset that transforms observations to new statistically independent linear forms. Infinite variance of non-Gaussian α-stable distributions makes algorithms like ICA non-appropriate for these distributions. In this note, we propose an algorithm which computes mixing matrix of ICA, in a parametric sub...

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implemen...

Multi-view face detection and recognition has been a challenging problem. The challenge is due to the fact that the distribution of multi-view faces in a feature space is more dispersed and more complicated than that of frontal faces. This paper presents an investigation into several view-subspace representations of multi-view faces, learned by using independent component analysis (ICA), indepe...

We present a toolbox for exploratory analysis of functional magnetic resonance imaging (fMRI) data using independent component analysis (ICA) within the widely used SPM analysis pipeline. The toolbox enables dimensional reduction using principal component analysis, ICA using several different ICA algorithms, selection of the number of components using the Bayesian information criterion (BIC), v...

Independent component analysis (ICA) is an unsupervised technique for blind source separation, and the ICA algorithms using nongaussianity as the measure of mutual independence have been also used for projection pursuit or visualization of multivariate data for knowledge discovery in databases (KDD). However, in real applications, it is often the case that we fail to extract useful latent varia...

Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources, and unlike principal component analysis (PCA), which uncorrelates the d...

This communication aims at giving some insights into the use of Independent Component Analysis (ICA) for solving biomedical problems. First the concept of ICA is reviewed and different classes of ICA methods are described. Next a survey on most encountered biomedical problems solved using ICA is detailed. Finally a comparative performance study of thirteen ICA algorithms is performed on biomedical

Principal Component Analysis (PCA) is often used for reducing the dimensionality of input feature space. However, the eigenspace based on PCA is not always the best feature space for pattern recognition. In this paper, we use the feature space based on Independent Component Analysis (ICA) and show that the ICA representation is more effective than the PCA representation for human action recogni...