Sparse Gaussian noisy independent component analysis

Frosti Palsson, Magnus O. Ulfarsson, Johannes R. Sveinsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

There are two main approaches to independent component analysis (ICA); maximization of non-Gaussianity of the sources and the exploitation of temporal correlation in Gaussian sources. In this paper, we present a novel sparse noisy ICA model where we have introduced temporal correlation in the sources, described by a first order auto regressive (AR(1)) process. The correlation structure of the sources eliminates the rotational invariance of the estimates, enabling their separation. Using simulated data, we demonstrate both source separation and denoising, where we compare our results to a sparse PCA method and the fastICA method. Additionally, we apply the method on a real hyperspectral dataset.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4224-4228
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 4 May 20149 May 2014

Publication series

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

Conference

Conference2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Country/TerritoryItaly
CityFlorence
Period4/05/149/05/14

Other keywords

  • Denoising
  • Independent Component Analysis
  • Noisy Principal Component Analysis
  • Source Separation
  • sparsity

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