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...

We present a class of algorithms that find clusters in independent component analysis: the data are linearly transformed so that the resulting components can be grouped into clusters, such that components are dependent within clusters and independent between clusters. In order to find such clusters, we look for a transform that fits the estimated sources to a forest-structured graphical model. ...

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...

Post-nonlinear (PNL) independent component analysis (ICA) is a generalisation of ICA where the observations are assumed to have been generated from independent sources by linear mixing followed by component-wise scalar nonlinearities. Most previous PNL ICA algorithms require the post-nonlinearities to be invertible functions. In this paper, we present a variational Bayesian approach to PNL ICA ...

Independent component analysis (ICA) in the frequency domain is used for solving the problem of blind source separation (BSS). However, this method has some problems. For example, a general ICA algorithm cannot determine the permutation of signals which is important in the frequency domain ICA. In this paper, we propose an approach to the solution for a permutation problem. The idea is to effec...

Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time...

Next generation image compression system should be optimized the way human vision system (HVS) works. HVS has been evolved over millions of years for the images which exist in our environment. This idea is reinforced by the fact that sparse codes extracted from natural images resemble the primary visual cortex of HVS. We have introduced a novel technique in which basis functions trained by Inde...

Group independent component analysis (ICA) with special assumptions is often used for analyzing functional magnetic resonance imaging (fMRI) data. Before ICA, dimension reduction is applied to separate signal and noise subspaces. For analyzing noisy fMRI data of individual participants in free-listening to naturalistic and long music, we applied individual ICA and therefore avoided the assumpti...