Chapter 21. Feature Extraction by ICA

  1. Aapo Hyvärinen,
  2. Juha Karhunen and
  3. Erkki Oja

Published Online: 15 MAY 2002

DOI: 10.1002/0471221317.ch21

Independent Component Analysis

Independent Component Analysis

How to Cite

Hyvärinen, A., Karhunen, J. and Oja, E. (2001) Feature Extraction by ICA, in Independent Component Analysis, John Wiley & Sons, Inc., New York, USA. doi: 10.1002/0471221317.ch21

Author Information

  1. Neural Networks Research Center, Helsinki University of Technology, Finland

Publication History

  1. Published Online: 15 MAY 2002
  2. Published Print: 21 MAY 2001

ISBN Information

Print ISBN: 9780471405405

Online ISBN: 9780471221319

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Keywords:

  • independent component analysis (ICA);
  • feature extraction;
  • sparse coding;
  • images;
  • estimating;
  • image denoising;
  • sparse code shrinkage;
  • independent subspaces;
  • topographic ICA;
  • neurophysiological connections

Summary

In this chapter, the authors consider a certain class of widely used signals, which they call natural images. This means images that we encounter in our lives all the time; images that depict wildlife scenes, human living environments, etc. The working hypothesis here is that this class is sufficiently homogeneous so that one can build a statistical model using observations of those signals, and then later use this model for processing the signals, for example, to compress or denoise them.