Generalised propagation for fast Fourier transforms with partial or missing data

AJ Storkey*

*Corresponding author for this work

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

Abstract

Discrete Fourier transforms and other related Fourier methods have been practically implementable due to the fast Fourier transform (FFT). However there are many situations where doing fast Fourier transforms without complete data would be desirable. In this paper it is recognised that formulating the FFT algorithm as a belief network allows suitable priors to be set for the Fourier coefficients. Furthermore efficient generalised belief propagation methods between clusters of four nodes enable the Fourier coefficients to be inferred and the missing data to be estimated in near to O (n log n) time, where n is the total of the given and missing data points. This method is compared with a number of common approaches such as setting missing data to zero or to interpolation. It is tested on generated data and for a Fourier analysis of a damaged audio signal.

Original languageEnglish
Title of host publicationADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
Subtitle of host publicationProceedings of the 2003 Conference
EditorsS Thrun, K Saul, B Scholkopf
Place of PublicationCAMBRIDGE
PublisherMIT Press
Pages433-440
Number of pages8
ISBN (Print)0-262-20152-6
Publication statusPublished - 2004
Event17th Annual Conference on Neural Information Processing Systems (NIPS) - , Canada
Duration: 8 Dec 2003 → …

Publication series

NameADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
PublisherM I T PRESS
Volume16
ISSN (Print)1049-5258

Conference

Conference17th Annual Conference on Neural Information Processing Systems (NIPS)
Country/TerritoryCanada
Period8/12/03 → …

Keywords

  • MODELS

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