Nonlinear Generalized Canonical Correlation Analysis by Neural Network Models

Yoshio Takane and Yuriko Oshima-Takane
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Yoshio Takane: McGill University
Yuriko Oshima-Takane: McGill University

A chapter in Measurement and Multivariate Analysis, 2002, pp 183-190 from Springer

Abstract: Summary 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.

Keywords: Hide Layer; Response Category; Output Activation; Canonical Correlation Analysis; Canonical Variate (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-65955-6_19

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DOI: 10.1007/978-4-431-65955-6_19

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