Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction.
Youngjoo KimAlexandru C. TeleaScott C. TragerJos B. T. M. RoerdinkPublished in: CoRR (2021)
Keyphrases
- dimensionality reduction
- high dimensional
- data points
- low dimensional
- high dimensionality
- high dimensional data
- visual data
- feature selection
- input space
- subspace clustering
- pattern recognition
- lower dimensional
- clustering algorithm
- manifold learning
- principal component analysis
- data representation
- nearest neighbor
- visual information
- random projections
- nonlinear dimensionality reduction
- dealing with high dimensional data
- feature space
- similarity search
- sparse data
- linear discriminant analysis
- dimensionality reduction methods
- feature extraction
- low dimensional spaces
- euclidean distance
- principal components
- small sample size
- embedding space
- visual features
- multi modal
- dimension reduction
- graph embedding
- low level
- locally linear embedding
- pattern recognition and machine learning
- kernel learning
- high dimensional spaces
- visual perception
- metric learning
- variable selection
- high dimensional datasets
- kernel function
- sparse representation
- metric space
- hierarchical clustering
- parameter space
- microarray data
- high level