High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction.
Kristjan H. GreenewaldBrian KingsburyYuancheng YuPublished in: ISIT (2023)
Keyphrases
- dimensionality reduction
- high dimensional
- low dimensional
- intrinsic dimensionality
- high dimensionality
- high dimensional data
- estimation problems
- pattern recognition and machine learning
- data points
- data representation
- principal component analysis
- principal components
- random projections
- dimension reduction
- input space
- information theory
- pattern recognition
- feature space
- feature selection
- mutual information
- similarity search
- linear discriminant analysis
- feature extraction
- lower dimensional
- sparse data
- dimensionality reduction methods
- manifold learning
- high dimensional problems
- structure preserving
- linear dimensionality reduction
- kernel function
- locally linear embedding
- high dimensional spaces
- dimensional data
- parameter estimation
- nearest neighbor
- variable selection
- euclidean distance
- sparse representation
- estimation algorithm
- least squares
- density estimation
- multi dimensional
- estimation process
- nonlinear dimensionality reduction
- computer vision
- machine learning
- image processing
- data sets