Moderate Dimension Reduction for k-Center Clustering.
Shaofeng H.-C. JiangRobert KrauthgamerShay SapirPublished in: SoCG (2024)
Keyphrases
- dimension reduction
- cluster analysis
- high dimensional data
- high dimensional data analysis
- unsupervised learning
- high dimensionality
- feature extraction
- high dimensional
- clustering algorithm
- principal component analysis
- dimensionality reduction
- low dimensional
- clustering method
- linear discriminant analysis
- high dimensional problems
- partial least squares
- k means
- data clustering
- data points
- singular value decomposition
- data mining and machine learning
- random projections
- variable selection
- feature selection
- feature subspace
- self organizing maps
- preprocessing
- manifold learning
- supervised learning
- discriminative information
- feature space
- nearest neighbor
- data analysis
- generative topographic mapping
- discriminant analysis
- spectral clustering
- principal components analysis
- machine learning
- sparse metric learning