A Robustification of ICA and Its Application to Signal Processing and Image Processing
Independent Component Analysis (ICA) is a powerful statistical method for Blind Source Separation (BSS) from the mixture data. It is widely used in signal processing like audio signal processing, image processing, biomedical signal processing as well as processing any time series data. The aims of ICA algorithms are to maximize the non-goussianity or minimize the dependency among the variables as ICA seeks to recover the sources that are as independent of each other as possible. The independence is a much stronger property than uncorrelatedness. Thus, ICA becomes more superior to the Principle Component Analysis (PCA).