Moderate Dimension Reduction for k-Center Clustering.
Shaofeng H.-C. JiangRobert KrauthgamerShay SapirPublished in: CoRR (2023)
Keyphrases
- dimension reduction
- cluster analysis
- high dimensional data analysis
- high dimensional data
- high dimensionality
- unsupervised learning
- high dimensional
- principal component analysis
- feature extraction
- clustering algorithm
- high dimensional problems
- clustering method
- feature selection
- manifold learning
- low dimensional
- linear discriminant analysis
- singular value decomposition
- partial least squares
- data points
- random projections
- data mining and machine learning
- k means
- feature subspace
- data clustering
- variable selection
- feature space
- preprocessing step
- discriminative information
- real world
- self organizing maps
- data sets
- discriminant analysis
- least squares
- distance metric
- data analysis
- manifold embedding
- generative topographic mapping
- microarray