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.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Gayle Leen, Colin Fyfe
• Keywords: ESANN 2006, ESANN 2007, Latent Variable, Latent Variable Model, Latent Variable Set