Grouping and dimensionality reduction by locally linear embedding.
Marzia PolitoPietro PeronaPublished in: NIPS (2001)
Keyphrases
- locally linear embedding
- dimensionality reduction
- nonlinear dimensionality reduction
- low dimensional
- manifold learning
- dimensional reduction
- high dimensional data
- high dimensional
- laplacian eigenmaps
- high dimensionality
- principal component analysis
- pattern recognition
- feature extraction
- dimensionality reduction methods
- subspace learning
- principal components analysis
- dimension reduction
- feature selection
- feature space
- data representation
- neighborhood preserving embedding
- linear discriminant analysis
- euclidean distance
- data points
- dimensional data
- neural network
- preprocessing step
- principal components
- diffusion maps
- locality preserving projections
- random projections
- underlying manifold
- semi supervised
- sparse representation
- feature vectors
- nearest neighbor
- singular value decomposition
- metric learning
- discriminant analysis