Sampling, Metric Entropy, and Dimensionality Reduction.
Dmitry BatenkovOmer FriedlandYosef YomdinPublished in: SIAM J. Math. Anal. (2015)
Keyphrases
- dimensionality reduction
- metric learning
- high dimensional data
- high dimensionality
- euclidean distance
- principal component analysis
- high dimensional
- normalized mutual information
- pattern recognition
- random sampling
- feature space
- random projections
- data representation
- mutual information
- low dimensional
- information content
- metric space
- feature extraction
- information theoretic
- data points
- information redundancy
- manifold learning
- kernel pca
- dimensionality reduction methods
- feature selection
- embedding space
- evaluation metrics
- distance metric
- linear discriminant analysis
- sampling algorithm
- structure preserving
- perceptual image quality
- sampling strategy
- locally linear embedding
- singular value decomposition
- sampling methods
- information theory
- sample size
- principal components
- kernel learning
- information entropy
- similarity measure
- image registration
- sampled data
- similarity search
- input space
- monte carlo