Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion.
Steven AtkinsonNicholas ZabarasPublished in: CoRR (2018)
Keyphrases
- data driven
- dimensionality reduction
- high dimensional
- gaussian process latent variable models
- low dimensional
- latent space
- high dimensional data
- high dimensionality
- manifold learning
- lower dimensional
- feature space
- data points
- pattern recognition
- principal component analysis
- euclidean distance
- random projections
- similarity search
- dimension reduction
- multi dimensional
- linear discriminant analysis
- higher dimensional
- nearest neighbor
- feature selection
- parameter space
- singular value decomposition
- sparse representation
- data sets
- high dimensional spaces
- bayesian networks
- image reconstruction
- maximum likelihood
- input data
- semi supervised
- feature extraction
- machine learning