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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]

  1. ^ Cardoso, Jean-François (Jan. 1999). "High-order contrasts for independent component analysis". Neural Computation. 11 (1): pp. 157—192. doi:10.1162/089976699300016863. {{cite journal}}: |page= has extra text (help); Check date values in: |date= (help)
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