Manifold Learning Regression with Non-stationary Kernels.
Alexander P. KuleshovAlexander BernsteinEvgeny BurnaevPublished in: ANNPR (2018)
Keyphrases
- non stationary
- manifold learning
- feature mapping
- reproducing kernel hilbert space
- feature space
- low dimensional
- support vector
- dimensionality reduction
- regression model
- nonlinear dimensionality reduction
- semi supervised
- high dimensional
- dimension reduction
- gaussian processes
- laplacian eigenmaps
- kernel function
- feature extraction
- subspace learning
- riemannian manifolds
- high dimensional data
- empirical mode decomposition
- manifold structure
- head pose estimation
- input space
- support vector regression
- autoregressive
- linear combination
- data sets
- locally linear embedding
- kernel methods
- euclidean space
- gaussian process
- multiple kernel learning
- principal component analysis
- sparse representation
- support vector machine svm
- computer vision