A Fast Gradient Approximation for Nonlinear Blind Signal Processing
When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data anal...
| Authors: | , |
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| Format: | article |
| Publication Date: | 2013 |
| Country: | España |
| Institution: | UVic-UCC |
| Repository: | RiUVic. Repositori institucional de la UVic-UCC |
| OAI Identifier: | oai:dspace.uvic.cat:10854/2603 |
| Online Access: | http://hdl.handle.net/10854/2603 https://doi.org/10.1007/s12559-012-9192-x |
| Access Level: | Open access |
| Keyword: | Tractament del senyal |
| Summary: | When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose. |
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