Keyphrases
- least squares
- embedding space
- laplacian eigenmaps
- graph embedding
- riemannian manifolds
- manifold learning
- nonlinear dimensionality reduction
- latent space
- euclidean space
- kernel pca
- low dimensional
- regularized least squares
- vector space
- input space
- geodesic distance
- high dimensional
- manifold embedding
- geometric structure
- dimensionality reduction
- manifold structure
- feature space
- label information
- reproducing kernel hilbert space
- locally linear embedding
- semi supervised
- optical flow
- lower dimensional
- kernel matrix
- hilbert space
- gaussian kernel
- output space
- kernel function
- data points
- kernel methods
- multi label
- class labels
- gaussian process latent variable models
- discriminant analysis
- graph laplacian
- density ratio
- gaussian processes
- locality preserving projections
- gaussian kernels
- parameter space
- nearest neighbor searching
- euclidean distance
- sparse representation
- high dimensional data
- head pose estimation
- kernel discriminant analysis
- domain adaptation