A parallel architecture using discrete wavelet transform for fast ICA implementation

Rong Bo Huang*, Yiu Ming CHEUNG, Shi Ming Zhu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

This paper utilizes a discrete wavelet transform to present a parallel architecture for independent component analysis (ICA), which is a hybrid system consisting of two sub-ICA processes. One process takes the high-frequency wavelet part of observations as its input, meanwhile the other process takes the low-frequency part. Their results are then merged to generate the final ICA results. Compared to the existing ICA algorithms, the proposed approach utilizes the full observation information, but the effective input length of the two parallel processes is halved. It therefore generally provides a new way for fast ICA implementation. In this paper, the experimental result has shown its success in extracting the independent components from a mixture.

Original languageEnglish
Title of host publicationProceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Pages1358-1361
Number of pages4
DOIs
Publication statusPublished - 2003
Event2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03 - Nanjing, China
Duration: 14 Dec 200317 Dec 2003

Publication series

NameProceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Volume2

Conference

Conference2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Country/TerritoryChina
CityNanjing
Period14/12/0317/12/03

Scopus Subject Areas

  • Signal Processing
  • Computer Networks and Communications

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