Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm

@article{Attias1998BlindSS,
  title={Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm},
  author={Hagai Attias and Christoph E. Schreiner},
  journal={Neural Computation},
  year={1998},
  volume={10},
  pages={1373-1424}
}
We derive a novel family of unsupervised learning algorithms for blind separation of mixed and convolved sources. Our approach is based on formulating the separation problem as a learning task of a spatiotemporal generative model, whose parameters are adapted iteratively to minimize suitable error functions, thus ensuring stability of the algorithms. The resulting learning rules achieve separation by exploiting high-order spatiotemporal statistics of the mixture data. Different rules are… CONTINUE READING
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Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures

  • R. Lambert
  • Unpublished doctoral dissertation,
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