Independent component analysis (ICA) has provided a new tool to analyze time series, which also gives rise to a question* how to order independent components? In the literature, some methods (Back and Trappenberg, Proceedings of International Joint Conference on Neural Networks, Vol. 2, 1999, pp. 989}992; HyvaK rinen, Neural Computing Surveys 2 (1999) 94; Back and Weigend, Int. J. Neural System...

The aim of blind source separation (BSS) is to transform a mixed random vector such that the original sources are recovered. If the sources are assumed to be statistically independent, independent component analysis (ICA) can be applied to perform BSS. An important aspect of successfully analysing data with BSS is to know the indeterminacies of the problem, that is how the separating model is r...

To save time, cost and labor, there are many studies that have been conducted about the detection of faults in industrial processes. Most of the previous studies used only Independent Component Analysis (ICA) or Principal Component Analysis (PCA) for detection, but they cannot form close enough boundaries to reject outliers. This paper proposes an ICA-based approach to detect outliers in a proc...

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...

A new proposal of blind channel estimation method for long term evoluation (LTE) based on combining advantages of denoising property of wavelet transform (WT) with blind estimation capability of independent component analysis (ICA) called wavelet denoising of ICA (WD-ICA) was presented. This new method increased the spectral efficiency compared to training based methods, and provided considerab...

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...