Keyphrases
- high dimensional data
- dimensionality reduction
- nonlinear dimensionality reduction
- low dimensional
- high dimensionality
- high dimensional
- high dimensions
- kernel pca
- nearest neighbor
- dimension reduction
- graph embedding
- original data
- data points
- input space
- data sets
- linear discriminant analysis
- principal component analysis
- similarity search
- subspace clustering
- manifold learning
- lower dimensional
- low dimensional manifolds
- low dimensional structure
- feature selection
- feature extraction
- pattern recognition
- subspace learning
- data analysis
- sparse representation
- input data
- high dimensional datasets
- dimensionality reduction methods
- euclidean distance
- underlying manifold
- laplacian eigenmaps
- high dimensional feature space
- locally linear embedding
- latent space
- feature space
- vector space
- principal components analysis
- dimensional data
- random projections
- principal components
- clustering high dimensional data
- feature vectors
- intrinsic dimensionality
- multi dimensional
- small sample size
- low rank
- supervised dimensionality reduction
- real world