Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A neural implementation of canonical correlation analysis
Neural Networks
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Active learning with extremely sparse labeled examples
Neurocomputing
Hi-index | 0.01 |
We consider several 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 with the second projection as the target for adapting the parameters of the first. The second uses a method which relies on probabilistically sphering the data, concatenating the two streams and then performing a probabilistic PCA. The third gets the canonical correlation projections directly without having to calculate the filters first. We also investigate the use of nonlinearity and a method for sparsification of these algorithms.