Independent Component Analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial-ICA (SICA) and temporal-ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

Abstract: Given a set of M signal mixtures (x1, x2, . . . , xM ) (e.g. microphone outputs), each of which is a different mixture of a set of M statistically independent source signals (s1, s2, . . . , sM ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically indepe...

We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression patterns, linear and nonlinear ICA finds components that are specific to certain biological processes. Genes that exhibit significant up-regulation or down-regulation within each component are grouped into clusters. We t...

Full-duplex communications systems that transmit and receive simultaneously suffer self-interference due to the mixing of transmitted signal weaker received at same node. The problem becomes compounded in Multi-Input Multi-Output (MIMO) systems, where considerable overhead is dedicated training. In this article, we discuss using blind source separation techniques, namely Independent Component A...

SUMMARY Independent component analysis (ICA) has recently been applied to epileptic seizure in the EEG. In this paper, the authors show how the fundamental axioms required for ICA to be valid are broken. Four common cases of childhood seizure are presented and assessed for stationarity and an eigenvalue analysis is applied. In all cases, for the stationary sections of data the eigenvalue analys...

The convex divergence is used as a surrogate function for obtaining a class of ICA algorithms (Independent Component Analysis) called the f-ICA. The convex divergence is a super class of α-divergence, which is a further upper family of Kullback-Leibler divergence or mutual information. Therefore, the f-ICA contains the α-ICA and the minimum mutual information ICA. In addition to theoretical int...

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...

ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the num...

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...