Article thumbnail
Location of Repository

Denoising source separation

By Mr Jaakko Särelä and Dr Harri Valpola

Abstract

A new algorithmic framework called denoising source separation (DSS) is introduced. The main benefit of this framework is that it allows for easy development of new source separation algorithms which are optimised for specific problems. In this framework, source separation algorithms are constucted around denoising procedures. The resulting algorithms can range from almost blind to highly specialised source separation algorithms. Both simple linear and more complex nonlinear or adaptive denoising schemes are considered. Some existing independent component analysis algorithms are reinterpreted within DSS framework and new, robust blind source separation algorithms are suggested. Although DSS algorithms need not be explicitly based on objective functions, there is often an implicit objective function that is optimised. The exact relation between the denoising procedure and the objective function is derived and a useful approximation of the objective function is presented. In the experimental section, various DSS schemes are applied extensively to artificial data, to real magnetoencephalograms and to simulated CDMA mobile network signals. Finally, various extensions to the proposed DSS algorithms are considered. These include nonlinear observation mappings, hierarchical models and overcomplete, nonorthogonal feature spaces. With these extensions, DSS appears to have relevance to many existing models of neural information processing

Topics: Statistical Models, Machine Learning, Neural Nets, Artificial Intelligence
Year: 2004
OAI identifier: oai:cogprints.org:3493

Suggested articles

Citations

  1. (1997). A blind source separation technique based on second order statistics.
  2. (2000). A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision research,
  3. (2004). A neurodynamical cortical model of visual attention and invariant object recognition.
  4. (2002). A resampling approach to estimate the stability of one- and multidimensional independent components.
  5. (2004). Accurate, fast and stable denoising source separation algorithms. Submitted to a conference,
  6. (1993). Basic principles, clinical applications, and related fields.
  7. (1998). Bayesian source separation and localization.
  8. (2002). Biomedical signal analysis: A case-study approach.
  9. (2001). Dynamical factor analysis of rhythmic magnetoencephalographic activity.
  10. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
  11. (1990). Forming sparse representations by local anti-hebbian learning.
  12. (1997). Fundamentals of nonlinear digital filtering.
  13. (1999). High-order contrasts for independent component analysis.
  14. (2002). ICA-RAKE switching for jammer cancellation in DS-CDMA array systems.
  15. (2000). Independent component analysis of biomedical signals.
  16. (1998). Kernel PCA pattern reconstruction via approximate pre-images.
  17. (1991). Learning invariance from transformation sequences.
  18. (2001). Natural signal statistics and sensory gain control.
  19. (1997). Neural networks for blind decorrelation of signals.
  20. (2001). Neurons with two sites of synaptic integration learn invariant representations.
  21. (1979). Optimal filtering.
  22. (1969). Optimization by Vector Space Methods.
  23. (1447). Overlearning in marginal distribution-based ICA: analysis and solutions.
  24. (1992). Principal components, minor components, and linear neural networks.
  25. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations.
  26. (1994). Representation and separation of signals using nonlinear PCA type learning.
  27. (2000). second international workshop on independent component analysis and blind signal separation, doi
  28. (1998). The FastICA MATLAB package.
  29. (1998). Transform invariant recognition by association in a recurrent network.
  30. (1995). Wavelet shrinkage: asymptopia?

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.