Joint Approximation Diagonalization of Eigen-matrices
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Joint Approximation Diagonalisation of Eigenmatrices (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 proxy measure for non-Gaussianity, which is used for defining independence between the source signals.
- ^ Cardoso, Jean-François (Jan. 1999). "High-order contrasts for independent component analysis". Neural Computation. 11 (1): pp. 157—192. doi:10.1162/089976699300016863.
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