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Extrinsic Geometrical Methods for Neural Blind Deconvolution
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fiori, S. |
| Copyright Year | 2006 |
| Abstract | The present contribution discusses a Riemannian-gradient -based algorithm and a projection-based learning algorithm over a curved paramet er space for single-neuron learning. We consider the ‘blind deconvolution’ signal processing prob lem. The learning rule naturally arises from a criterion-function minimization over the unitary hy per-sphere setting. We consider the blind deconvolution performances of the two algorithms as w ell as their computational burden and numerical features. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://djafari.free.fr/maxent2006/Finals/092_Fiori.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Blind deconvolution Computation Gradient KDM5D gene Learning rule Neuron Numerical analysis Performance Problem assaults/harmful events Signal processing Visually Impaired Persons |
| Content Type | Text |
| Resource Type | Article |
