Name
Title | ||
|---|---|---|
A Gaussian process latent variable model formulation of canonical correlation analysis |
Abstract | ||
|---|---|---|
We investigate a nonparametric model with which to vi- sualize the relationship between two datasets. We base our model on Gaussian Process Latent Variable Models (GPLVM)(1),(2), a probabilisti- cally 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 corre- lations between the datasets, resulting in a GPLVM formulation of canon- ical correlation analysis which can be nonlinearised by choice of covariance function. |
Year | Venue | Keywords |
|---|---|---|
2006 | ESANN | latent variable,latent variable model,canonical correlation analysis,gaussian process,covariance function |
Field | DocType | Citations |
Covariance function,Pattern recognition,Gaussian process latent variable model,Canonical correlation,Latent variable model,Latent class model,Latent variable,Probabilistic latent semantic analysis,Artificial intelligence,Gaussian process,Mathematics | Conference | 7 |
PageRank | References | Authors |
0.66 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gayle Leen | 1 | 58 | 7.35 |
| Colin Fyfe | 2 | 508 | 55.62 |