Comparison of Principal Component Analysis and Independedent Component Analysis for Blind Source Separation

Our contribution briefly outlines the basics of the well-established technique in data mining, namely the principal component analysis (PCA), and a rapidly emerging novel method, that is, the independent component analysis (ICA). The performance of PCA singular value decomposition-based and stationary linear ICA in blind separation of artificially generated data out of linear mixtures was critically evaluated and compared. All our results outlined the superiority of ICA relative to PCA in faithfully retrieval of the original independent source components.