Name
Abstract | ||
|---|---|---|
We consider two stochastic process methods for performing canonical correlation analysis (CCA). The flrst 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. |
Year | Venue | Keywords |
|---|---|---|
2006 | ESANN | gaussian process,canonical correlation analysis,stochastic process |
Field | DocType | Citations |
Applied mathematics,Canonical correlation,Continuous-time stochastic process,Artificial intelligence,Gaussian process,Mathematical optimization,Dirichlet process,Pattern recognition,Gaussian random field,Stochastic process,Gaussian,Ornstein–Uhlenbeck process,Mathematics | Conference | 8 |
PageRank | References | Authors |
0.75 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Colin Fyfe | 1 | 508 | 55.62 |
| Gayle Leen | 2 | 58 | 7.35 |