Sketching, Embedding, and Dimensionality Reduction for Information Spaces.
Amirali AbdullahRavi KumarAndrew McGregorSergei VassilvitskiiSuresh VenkatasubramanianPublished in: CoRR (2015)
Keyphrases
- information space
- dimensionality reduction
- nonlinear dimensionality reduction
- graph embedding
- structure preserving
- embedding space
- locality preserving projections
- multidimensional scaling
- low dimensional
- laplacian eigenmaps
- low dimensional spaces
- manifold learning
- high dimensional data
- neighborhood preserving
- principal component analysis
- high dimensional
- latent space
- pattern recognition
- vector space
- input space
- data representation
- information overload
- data points
- subspace learning
- feature extraction
- pattern recognition and machine learning
- data hiding
- dimensionality reduction methods
- feature space
- locally linear embedding
- high dimensionality
- random projections
- euclidean space
- linear discriminant analysis
- neural network
- metric learning
- sketch recognition
- principal components
- data embedding
- dimension reduction
- linear dimensionality reduction
- feature selection
- intrinsic dimensionality
- pairwise
- high dimensional spaces
- watermarking algorithm
- data sets