High-dimensional Bayesian optimization using low-dimensional feature spaces.
Riccardo MoriconiMarc Peter DeisenrothK. S. Sesh KumarPublished in: Mach. Learn. (2020)
Keyphrases
- high dimensional
- low dimensional
- feature space
- dimensionality reduction
- high dimensional data
- data points
- principal component analysis
- manifold learning
- dimension reduction
- lower dimensional
- high dimensionality
- similarity search
- high dimensional data space
- nearest neighbor search
- training samples
- maximum likelihood
- optimization algorithm
- high dimensions
- nearest neighbor
- kernel function
- low dimensional spaces
- latent space
- high dimensional spaces
- euclidean space
- input space
- optimization problems
- optimization method
- parameter space
- pairwise
- training set
- multi dimensional
- locally linear embedding
- kernel methods
- graph embedding
- embedding space
- linear dimensionality reduction
- feature extraction