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2006

A Gaussian process latent variable model formulation of canonical correlation analysis

8 years 5 months ago
A Gaussian process latent variable model formulation of canonical correlation analysis
Abstract. We investigate a nonparametric model with which to visualize the relationship between two datasets. We base our model on Gaussian Process Latent Variable Models (GPLVM)[1],[2], a probabilistically defined latent variable model which takes the alternative approach of marginalizing the parameters and optimizing the latent variables; we optimize a latent variable set for each dataset, which preserves the correlations between the datasets, resulting in a GPLVM formulation of canonical correlation analysis which can be nonlinearised by choice of covariance function.
Gayle Leen, Colin Fyfe
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Gayle Leen, Colin Fyfe
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