Composition of Local Normal Coordinates and Polyhedral Geometry in Riemannian Manifold Learning.
Gastão Florêncio Miranda Jr.Gilson A. GiraldiCarlos E. ThomazDaniel MillanPublished in: Int. J. Nat. Comput. Res. (2015)
Keyphrases
- manifold learning
- geodesic distance
- low dimensional manifolds
- low dimensional
- dimensionality reduction
- diffusion maps
- semi supervised
- nonlinear dimensionality reduction
- high dimensional
- dimension reduction
- subspace learning
- laplacian eigenmaps
- high dimensional data
- riemannian manifolds
- feature extraction
- underlying manifold
- shape space
- sparse representation
- relative position
- feature space
- linear subspace
- manifold structure
- euclidean space
- high dimensionality
- pairwise
- machine learning