The Sphere-Concatenate Method for Gaussian Process Canonical Correlation Analysis

  • Pei Ling Lai
  • Gayle Leen
  • Colin Fyfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


We have recently developed several ways of using Gaussian Processes to perform Canonical Correlation Analysis. We review several of these methods, introduce a new way to perform Canonical Correlation Analysis with Gaussian Processes which involves sphering each data stream separately with probabilistic principal component analysis (PCA), concatenating the sphered data and re-performing probabilistic PCA. We also investigate the effect of sparsifying this last method. We perform a comparative study of these methods.


Data Stream Gaussian Process Canonical Correlation Analysis Kernel Principal Component Analysis Neural Network Method 
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 2006

Authors and Affiliations

  • Pei Ling Lai
    • 1
  • Gayle Leen
    • 2
  • Colin Fyfe
    • 2
  1. 1.Southern Taiwan Institute of TechnologyTaiwan
  2. 2.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

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