• Assume the probability density functions of the underlying independent components are Gaussian or super-Gaussian

• Choose a sigmoid function , , and update with until converges

• Change the training feature vectors into the new space, then get the mean and covariance matrices for each class

• Change the testing feature vectors into the new space separately and calculate its distances to each class and give classification