A General Framework for Learning in the Complex Domain
نویسنده
چکیده
We present a framework that greatly simplifies the evaluations and analyses for optimization in the complex plane through the use of a generalized definition of analyticity. We show how the gradient, relative (natural) gradient, Newton, and Newton variation updates can be easily derived within the framework. We demonstrate application of the framework for training of a multi-layer perceptron network and for performing complex-valued independent component analysis.
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