Independent component analysis

found: International Conference, AVBPA (3rd : 2001 : Halmstad, Sweden). Audio-and video-based biometric person authentication, 2001:p. 60 (Independent component analysis is a technique for extracting statistically independent variables from a mixture of them. ICA has been successfully applied to many different problems such as MEG and EEG data analysis, finding hidden factors in financial data, and face recognition)

found: Hyvärinen, A. Independent component analysis, 2001:p. 1 (Independent component analysis (ICA) is a method for finding underlying factors or components from multivariate (multidimensional) statistical data. What distinguishes ICA from other methods is that it looks for components that are both statistically independent, and nongaussian)

found: Human brain mapping, Jan. 2006:p. 48 (Independent component analysis is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals)

found: IEEE engineering in medicine and biology magazine, Mar.-Apr. 2006:p. 79 (Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources, and unlike principal component analysis (PCA), which uncorrelates the data, ICA works with higher-order statistics to achieve independence)