Embedding new data points for manifold learning via coordinate propagation.
Shiming XiangFeiping NieYangqiu SongChangshui ZhangChunxia ZhangPublished in: Knowl. Inf. Syst. (2009)
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
- subspace learning
- locality preserving projections
- euclidean space
- semi supervised
- latent space
- dimension reduction
- graph embedding
- nearest neighbor
- geodesic distance
- vector space
- manifold embedding
- nonlinear manifold learning
- feature extraction
- manifold structure
- feature space
- locally linear embedding
- neighborhood graph
- high dimensionality
- sparse representation
- input space
- head pose estimation
- data distribution
- euclidean distance
- image processing
- computer vision
- intrinsic dimensionality
- dimensional data
- data sets
- linear discriminant analysis
- pattern recognition
- discriminant embedding