Principal Component Analysis for Gaussian Process Posteriors.
Hideaki IshibashiShotaro AkahoPublished in: Neural Comput. (2022)
Keyphrases
- gaussian process
- principal component analysis
- gaussian processes
- hyperparameters
- posterior distribution
- bayesian framework
- regression model
- latent variables
- dimensionality reduction
- gaussian process regression
- bayesian inference
- approximate inference
- low dimensional
- semi supervised
- model selection
- gaussian process classification
- covariance matrix
- posterior probability
- gaussian process models
- covariance function
- feature extraction
- face recognition
- sparse approximations
- feature space
- lower dimensional
- closed form
- machine learning
- markov random field
- pairwise
- feature selection
- expectation propagation
- support vector
- denoising
- higher order
- maximum likelihood