Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models.
Katiana KontolatiDimitrios LoukrezisKetson R. M. dos SantosDimitrios G. GiovanisMichael D. ShieldsPublished in: CoRR (2021)
Keyphrases
- manifold learning
- high dimensional
- low dimensional
- dimensionality reduction
- high dimensional data
- nonlinear dimensionality reduction
- dimension reduction
- data points
- diffusion maps
- low dimensional manifolds
- subspace learning
- high dimensionality
- head pose estimation
- embedding space
- similarity search
- semi supervised
- locally linear embedding
- sparse representation
- nearest neighbor
- laplacian eigenmaps
- feature space
- neural network
- manifold structure
- manifold embedding
- noisy data
- kernel function
- feature extraction
- latent space
- high dimensional spaces
- metric space
- parameter space
- image features
- feature vectors
- pattern recognition