Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems
Abstract
In this paper we discuss the semi parametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of non(cid:173) causal FIR filters. Then blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. A natural gradient learn(cid:173) ing algorithm is developed for training noncausal filters. Stability of the natural gradient algorithm is also analyzed in this framework.
Cite
Text
Zhang et al. "Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems." Neural Information Processing Systems, 1999.Markdown
[Zhang et al. "Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/zhang1999neurips-semiparametric/)BibTeX
@inproceedings{zhang1999neurips-semiparametric,
title = {{Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems}},
author = {Zhang, Liqing and Amari, Shun-ichi and Cichocki, Andrzej},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {363-369},
url = {https://mlanthology.org/neurips/1999/zhang1999neurips-semiparametric/}
}