Joint Approximation Diagonalization of Eigen-matrices (JADE) is an algorithm for independent component analysis that separates observed mixed signals into latent source signals by exploiting fourth order moments.
[1] The fourth order moments are a measure of non-Gaussianity, which is used as a proxy for defining independence between the source signals.
The motivation for this measure is that Gaussian distributions possess zero excess kurtosis, and with non-Gaussianity being a canonical assumption of ICA, JADE seeks an orthogonal rotation of the observed mixed vectors to estimate source vectors which possess high values of excess kurtosis.
denote an observed data matrix whose
dimensional identity matrix, that is, Applying JADE to
entails to estimate the source components given by the rows of the
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