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Noisy independent component analysis of auto-correlated components

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  • Jakob Knollmuller
  • Torsten A. En{ss}lin

Abstract

We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high noise regime. Characteristics of the measurement instruments can be included, allowing for application in complex measurement situations. Independent posterior samples can be provided, permitting error estimates on all desired quantities. Using the concept of information field theory, the algorithm is not restricted to any dimensionality of the underlying space or discretization scheme thereof.

Suggested Citation

  • Jakob Knollmuller & Torsten A. En{ss}lin, 2017. "Noisy independent component analysis of auto-correlated components," Papers 1705.02344, arXiv.org, revised Aug 2017.
    • Handle: RePEc:arx:papers:1705.02344
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    File URL: http://arxiv.org/pdf/1705.02344
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