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2006

Stochastic Processes for Canonical Correlation Analysis

8 years 5 months ago
Stochastic Processes for Canonical Correlation Analysis
We consider two stochastic process methods for performing canonical correlation analysis (CCA). The first uses a Gaussian Process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat in the opposite direction. The second uses a Dirichlet process of Gaussian models where the Gaussian models are determined by Probabilistic CCA [1]. The latter method is more computationally intensive but has the advantages of non-parametric approaches.
Colin Fyfe, Gayle Leen
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Colin Fyfe, Gayle Leen
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