creators_name: Särelä, Jaakko
creators_name: Valpola, Harri
type: preprint
datestamp: 2004-03-16
lastmod: 2011-03-11 08:55:29
metadata_visibility: show
title: Denoising source separation
subjects: comp-sci-stat-model
subjects: comp-sci-mach-learn
subjects: comp-sci-neural-nets
subjects: comp-sci-art-intel
full_text_status: public
keywords: blind source separation, BSS, prior information, denoising,
denoising source separation, DSS, independent component analysis, ICA, magnetoencephalograms, MEG, CDMA
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.
date: 2004-02
date_type: published
refereed: FALSE
citation:   Särelä, Mr Jaakko and Valpola, Dr Harri  (2004) Denoising source separation.  [Preprint]     
document_url: http://cogprints.org/3493/1/JS_DSS.pdf