DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling.
Gautam PaiRonen TalmonAlexander M. BronsteinRon KimmelPublished in: WACV (2019)
Keyphrases
- manifold learning
- geodesic distance
- high dimensional
- sparse representation
- low dimensional
- dimensionality reduction
- nonlinear dimensionality reduction
- dimension reduction
- subspace learning
- diffusion maps
- random projections
- random sampling
- feature extraction
- laplacian eigenmaps
- sparse coding
- parameter space
- semi supervised
- feature space
- underlying manifold
- high dimensional data
- riemannian manifolds
- manifold structure
- distance transform
- face recognition
- shortest path
- locally linear embedding
- support vector
- low dimensional manifolds
- nonlinear manifold