Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning.
Lucas Pascotti ValemDaniel Carlos Guimarães PedronetteLongin Jan LateckiPublished in: CoRR (2023)
Keyphrases
- manifold learning
- nonlinear dimensionality reduction
- laplacian eigenmaps
- manifold embedding
- embedding space
- semi supervised
- low dimensional
- geodesic distance
- dimensionality reduction
- discriminant embedding
- low dimensional manifolds
- latent space
- high dimensional
- high dimensional data
- feature extraction
- dimension reduction
- feature space
- sparse representation
- vector space
- graph embedding
- maximum margin
- kernel pca
- data sets
- locally linear embedding
- semi supervised learning
- pattern recognition
- image processing