1. Algorithm

2. References

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.

Algorithm

Let denote an observed data matrix whose columns correspond to observations of -variate mixed vectors. It is assumed that is prewhitened, that is, its rows have a sample mean equaling zero and a sample covariance is the dimensional identity matrix, that is,

.

Applying JADE to entails

1. computing fourth-order cumulants of and then
2. optimizing a contrast function to obtain a rotation matrix

to estimate the source components given by the rows of the dimensional matrix .^[2]

References

• Loûre
• Loćika (Rekovac)
• Ločica Mass Grave
• Ločice 1 Mass Grave
• Ločice 2 Mass Grave
• Ločil bom peno od valov
• Loşna River
• Loška Dolina
• Loška dolina
• Loška Dolina Municipality
• Loška Dolina, Slovenia
• Loška Gora, Radeče
• Loška Vas
• Loška vas
• Loški Potok
• Loški Potok Municipality
• Loško polje
• Lošėjas
• Loţu
• Lož Karst Field
• Lož Valley
• Loțu
• Loʻihi
• Loʻihi Seamount
• L^p