Semiparametric approach to multichannel blind deconvolution of nonminimum phase systems

L. Q. Zhang, S. Amari, A. Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper we discuss the semiparametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of non-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 learning algorithm is developed for training noncausal filters. Stability of the natural gradient algorithm is also analyzed in this framework.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999
PublisherNeural information processing systems foundation
Pages364-369
Number of pages6
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 29 Nov 19994 Dec 1999

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

Conference13th Annual Neural Information Processing Systems Conference, NIPS 1999
Country/TerritoryUnited States
CityDenver, CO
Period29/11/994/12/99

Fingerprint

Dive into the research topics of 'Semiparametric approach to multichannel blind deconvolution of nonminimum phase systems'. Together they form a unique fingerprint.

Cite this