Extendable and invertible manifold learning with geometry regularized autoencoders.
Andrés F. DuqueSacha MorinGuy WolfKevin R. MoonPublished in: CoRR (2020)
Keyphrases
- manifold learning
- low dimensional manifolds
- geodesic distance
- low dimensional
- nonlinear dimensionality reduction
- dimensionality reduction
- semi supervised
- denoising
- diffusion maps
- dimension reduction
- underlying manifold
- high dimensional
- least squares
- sparse representation
- laplacian eigenmaps
- feature extraction
- high dimensional data
- manifold learning algorithm
- subspace learning
- manifold structure
- manifold embedding
- linear subspace
- head pose estimation
- nonlinear manifold
- feature space
- riemannian manifolds
- geometric structure
- semi supervised learning
- principal component analysis
- pattern recognition
- computer vision
- data sets