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Two-dimensional joint process adaptive filtering via principal component support region

Kim, D.I., De Wilde, P. (2002) Two-dimensional joint process adaptive filtering via principal component support region. In: Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop. . pp. 545-553. IEEE, Madison, WI, USA ISBN 0-7803-5673-X. (doi:10.1109/NNSP.1999.788174) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:58058)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1109/NNSP.1999.788174

Abstract

This paper describes a 2-D joint process adaptive filtering algorithm using the orthogonal property of the principal component support region in order to speed up the convergence rate. We also introduce the symmetrical sparse support region (SSSR) to reduce the computational burden of the duplicated support region (DSR). Computer simulation results are given to verify the performance of the proposed model.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/NNSP.1999.788174
Uncontrolled keywords: Computational complexity; Computer simulation; Eigenvalues and eigenfunctions; Image reconstruction; Matrix algebra; Neural networks, Adaptive filtering algorithm; Duplicated support region; Least mean square; Symmetrical sparse support region, Adaptive algorithms
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 04 Jan 2023 09:32 UTC
Last Modified: 05 Jan 2023 16:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58058 (The current URI for this page, for reference purposes)

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