A Canonical Correlation Analysis Based Method for Improving BSS of Two Related Data Sets

  • Juha Karhunen
  • Tele Hao
  • Jarkko Ylipaavalniemi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)

Abstract

We consider an extension of ICA and BSS for separating mutually dependent and independent components from two related data sets. We propose a new method which first uses canonical correlation analysis for detecting subspaces of independent and dependent components. Different ICA and BSS methods can after this be used for final separation of these components. Our method has a sound theoretical basis, and it is straightforward to implement and computationally not demanding. Experimental results on synthetic and real-world fMRI data sets demonstrate its good performance.

Keywords

Singular Value Decomposition Independent Component Analysis Data Vector Canonical Correlation Analysis Singular Vector 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Juha Karhunen
    • 1
  • Tele Hao
    • 1
  • Jarkko Ylipaavalniemi
    • 1
  1. 1.Dept. of Information and Computer Science, School of ScienceAalto UniversityAaltoFinland

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