Overcomplete Independent Component Analysis via Linearly Constrained Minimum Variance Spatial Filtering

  title={Overcomplete Independent Component Analysis via Linearly Constrained Minimum Variance Spatial Filtering},
  author={Moritz Grosse-Wentrup and Martin Buss},
  journal={VLSI Signal Processing},
Independent Component Analysis (ICA) designed for complete bases is used in a variety of applications with great success, despite the often questionable assumption of having N sensors and M sources with NQM. In this article, we assume a source model with more sources than sensors (M>N), only L<N of which are assumed to have a non-Gaussian distribution. We argue that this is a realistic source model for a variety of applications, and prove that for ICA algorithms designed for complete bases (i.e… CONTINUE READING


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