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Hierarchical Extraction of Independent Subspaces of Unknown Dimensions

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5441)

Abstract

Independent Subspace Analysis (ISA) is an extension of Independent Component Analysis (ICA) that aims to linearly transform a random vector such as to render groups of its components mutually independent. A recently proposed fixed-point algorithm is able to locally perform ISA if the sizes of the subspaces are known, however global convergence is a serious problem as the proposed cost function has additional local minima. We introduce an extension to this algorithm, based on the idea that the algorithm converges to a solution, in which subspaces that are members of the global minimum occur with a higher frequency. We show that this overcomes the algorithm’s limitations. Moreover, this idea allows a blind approach, where no a priori knowledge of subspace sizes is required.

Keywords

  • Independent Component Analysis
  • Independent Component Analysis
  • Minimum Description Length
  • Independent Component Analysis Algorithm
  • Unknown Dimension

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2009 Springer-Verlag Berlin Heidelberg

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Gruber, P., Gutch, H.W., Theis, F.J. (2009). Hierarchical Extraction of Independent Subspaces of Unknown Dimensions. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_33

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  • DOI: https://doi.org/10.1007/978-3-642-00599-2_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00598-5

  • Online ISBN: 978-3-642-00599-2

  • eBook Packages: Computer ScienceComputer Science (R0)