Extrinsic Geometrical Methods for Neural Blind Deconvolution
نویسنده
چکیده
The present contribution discusses a Riemannian-gradient-based algorithm and a projection-based learning algorithm over a curved parameter space for single-neuron learning. We consider the ‘blind deconvolution’ signal processing problem. The learning rule naturally arises from a criterion-function minimization over the unitary hyper-sphere setting. We consider the blind deconvolution performances of the two algorithms as well as their computational burden and numerical features.
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