Signal decomposition using multiscale admixture models

Matus Telgarsky, John Lafferty

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

Abstract

Admixture models are "mixtures of mixtures" that decompose an object into multiple latent components, with the component proportions varying stochastically across objects. Recent work in machine learning has successfully developed admixture models for text, and work in population genetics has developed such models to analyze complex groups of individuals having mixed ancestry. We introduce a family of graphical admixture models for decomposing a signal into multiple components based on a wavelet representation of the signal. Two models are developed, one using a fixed segmentation of the signal, another using recursive dyadic partitioning. Variational algorithms are derived for inferring mixture proportions and estimating parameters.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesII449-II452
DOIs
StatePublished - Aug 6 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Graphical model
  • Recursive dyadic partitioning
  • Unsupervised signal segmentation and labeling
  • Variational inference
  • Wavelets

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Signal decomposition using multiscale admixture models'. Together they form a unique fingerprint.

Cite this