Fast adaptive algorithms using eigenspace projections

N. Gopalan Nair, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Although adaptive gradient algorithms are simple and relatively robust, they generally have poor performance in the absence of "rich" excitation. In particular, it is well known that the convergence speed of the LMS algorithm deteriorates when the condition number of the input autocorrelation matrix is large. This problem has been previously addressed using weighted RLS or normalized frequency-domain algorithms. In this paper, we present a new approach that employs gradient projections in selected eigenvector sub-spaces to improve the convergence properties of IAIS algorithms for colored inputs. We also introduce an efficient method to iteratively update an "eigen subspace" of the autocorrelation matrix. The proposed algorithm is more efficient, in terms of computational complexity, than the WRLS and its convergence speed approaches that of the WRLS even for highly correlated inputs.

Original languageEnglish (US)
Title of host publicationConference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)0818664053
StatePublished - 1994
Event28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994 - Pacific Grove, United States
Duration: Oct 31 1994Nov 2 1994

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
Country/TerritoryUnited States
CityPacific Grove

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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