Embedding New Data Points for Manifold Learning Via Coordinate Propagation.
Shiming XiangFeiping NieYangqiu SongChangshui ZhangChunxia ZhangPublished in: PAKDD (2007)
Keyphrases
- manifold learning
- embedding space
- data points
- nonlinear dimensionality reduction
- low dimensional
- low dimensional manifolds
- dimensionality reduction
- laplacian eigenmaps
- high dimensional data
- high dimensional
- data embedding
- diffusion maps
- semi supervised
- graph embedding
- manifold embedding
- geodesic distance
- nonlinear manifold learning
- head pose estimation
- locality preserving projections
- feature space
- subspace learning
- vector space
- dimension reduction
- manifold structure
- input space
- euclidean space
- neighborhood graph
- data distribution
- nearest neighbor
- latent space
- euclidean distance
- unsupervised learning
- riemannian manifolds
- sparse representation
- distance function
- input data
- image processing
- discriminant embedding
- clustering algorithm
- face images
- neural network
- subspace methods