Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Independent component analysis: algorithms and applications
Neural Networks
Independent Component Analysis: A Tutorial Introduction
Independent Component Analysis: A Tutorial Introduction
Independent component analysis for artefact separation in astrophysical images
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Bayesian predictive kernel discriminant analysis
Pattern Recognition Letters
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Independent Component Analysis (ICA) is closely related to Principal Component Analysis (PCA) and factor analysis. Whereas ICA finds a set of source data that are mutually independent, PCA finds a set of data that are mutually uncorrelated. The assumption that data from different physical processes are uncorrelated does not always imply the reverse case that uncorrelated data are coming from different physical processes. This is because lack of correlation is a weaker property than independence. In the present case an objective classification of the globular clusters (GCs) of NGC 5128 has been carried out. Components responsible for significant variation have been obtained through both Principal Component Analysis (PCA) and Independent Component Analysis (ICA) and the classification has been done by K-means clustering. The set of observable parameters includes structural parameters, spectroscopically determined Lick indices and radial velocities from the literature. We propose that GCs of NGC 5128 consist of two groups. One group originated in the original cluster formation event that coincided with the formation of the elliptical galaxy and the other group emerged from an accreted spiral galaxy. This is unlike the previous result (Chattopadhyay et al., 2009) which accounts for a third group originating from the accretion of tidally stripped dwarf galaxies.