Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion.
Steven AtkinsonNicholas ZabarasPublished in: J. Comput. Phys. (2019)
Keyphrases
- data driven
- dimensionality reduction
- high dimensional
- gaussian process latent variable models
- low dimensional
- latent space
- high dimensionality
- high dimensional data
- feature space
- data points
- lower dimensional
- dimension reduction
- similarity search
- manifold learning
- random projections
- image reconstruction
- singular value decomposition
- linear discriminant analysis
- multi dimensional
- euclidean distance
- principal component analysis
- parameter space
- feature selection
- high dimensional spaces
- pattern recognition
- sparse representation
- feature extraction
- data sets
- nearest neighbor
- euclidean space
- bayesian inference
- image processing
- kernel function