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Identifiability, Subspace Selection and Noisy ICA

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

We consider identifiability and subspace selection for the ICA model with additive Gaussian noise. We discuss a canonical decomposition that allows us to decompose the system into a signal and a noise subspace and show that an unbiased estimate of these can be obtained using a standard ICA algorithm. This can also be used to estimate the relevant subspace dimensions and may often be preferable to PCA dimension reduction. Finally we discuss the identifiability issues for the subsequent ‘square’ noisy ICA model after projection.

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Author information

Authors and Affiliations

  1. DSP & Multimedia Group, Queen Mary University of London, Mile End Road, London, E1 4NS, UK

    Mike Davies

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  1. Mike Davies
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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Davies, M. (2004). Identifiability, Subspace Selection and Noisy ICA. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_20

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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