A complex valued Hebbian learning algorithm

We present a training rule for a single-layered linear network with complex valued weights and activation levels. This network can be used to extract the principal components of a complex valued data set. We also introduce a new training method that reduces the training time of the complex valued as well as of the real valued network. The use of the new network and training algorithm is illustrated with a problem of compressing images represented in the spectral domain.