Nonlinear Generalized Canonical Correlation Analysis by Neural Network Models

  • Yoshio Takane
  • Yuriko Oshima-Takane


A method of K-set canonical correlation analysis capable of joint multivariate nonlinear transformations of data was proposed. The method consists of K nonlinear data transformation modules, each of which is a multi-layered feed-forward network, and one integrator module which combines information from the K transformation modules. The proposed method is useful for integrating information from K concurrent sources.


Hide Layer Response Category Output Activation Canonical Correlation Analysis Canonical Variate 
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Copyright information

© Springer Japan 2002

Authors and Affiliations

  • Yoshio Takane
    • 1
  • Yuriko Oshima-Takane
    • 1
  1. 1.McGill UniversityMontrealCanada

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