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Two Methods for Sparsifying Probabilistic Canonical Correlation Analysis

  • Colin Fyfe
  • Gayle Leen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

We have recently developed several ways of performing Canonical Correlation Analysis [1,5,7,4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3,2]. In this paper, we examine two recent developments which sparsify probabilistic methods of performing canonical correlation analysis.

Keywords

Data Stream Canonical Correlation Canonical Correlation Analysis Kernel Principal Component Analysis 14th European Symposium 
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

  • Colin Fyfe
    • 1
  • Gayle Leen
    • 1
  1. 1.Applied Computational Intelligence Research UnitThe University of PaisleyScotland

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