Independent Component Analysis

Pierre Comon (1)

1 Thomson Sintra

The Independent Component Analysis (ICA) of a random vector consists of searching for the linear transformation that minimizes the statistical dependence between its components. In order to design a practical optimization criterion, the expression of mutual information is being resorted to, as a function of cumulants. The concept of ICA may be seen as an extension of Principal Component Analysis, which only imposes independence up to second order and consequently defines directions that are orthogonal. Applications of ICA include data compression, detection and localization of sources, or blind identification and deconvolution.

Mots clés: skewness; non Gaussian; cumulants; contrast; Edgeworth expansion; Mutual Information; kurtosis; sweeping algorithm; Jacobi; Independent Component Analysis; ICA

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Pierre Comon. Independent Component Analysis. J-L.Lacoume. Higher-Order Statistics, Elsevier, pp.29-38, 1992. ⟨hal-00346684⟩