Principal Component Analysis for High Dimension Stochastic Gaussian Process Model Fitting.
Maxime XuerebTian Ming HuoSzu Hui NgPublished in: IEEM (2019)
Keyphrases
- gaussian process
- model fitting
- high dimension
- principal component analysis
- feature space
- real valued
- low dimensional
- parameter estimation
- least squares
- dimensionality reduction
- high dimensional
- regression model
- model selection
- latent variables
- input space
- shape model
- hyperparameters
- feature selection
- bayesian framework
- high dimensional data
- semi supervised
- active appearance models
- support vector machine
- feature extraction
- expectation maximization
- face recognition
- data sets
- cross validation
- probabilistic model
- bayesian inference