Independent component analysis

Independent component analysis (ICA) is a relatively recently developed method. In signal processing, independent component analysis is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that the subcomponents are non-Gaussian signals and that they are statistically independent from each other, or as independent as possible. Such a representation seems to capture the essential structure of data in many applications as well, for example in images and can then be used for feature extraction.

ICA is related to PCA, but is a much more powerful technique that is capable of finding the underlying factors of sources when the more classic methods fail. It can be applied to digital images, document databases, economic indicators and for psychometric measurements.

  • Last modified: 2019/09/07 22:57