Randomized Dimensionality Reduction for k-Means Clustering.
Christos BoutsidisAnastasios ZouziasMichael W. MahoneyPetros DrineasPublished in: IEEE Trans. Inf. Theory (2015)
Keyphrases
- dimensionality reduction
- high dimensional
- high dimensional data
- feature selection
- principal component analysis
- pattern recognition
- pattern recognition and machine learning
- data representation
- feature extraction
- low dimensional
- linear dimensionality reduction
- manifold learning
- k means
- input space
- decision forest
- random projections
- preprocessing step
- high dimensionality
- linear discriminant analysis
- singular value decomposition
- principal components
- data points
- euclidean distance
- structure preserving
- partitioned data
- lower dimensional
- dimensionality reduction methods
- diffusion maps
- metric learning
- feature space
- locally linear embedding
- spectral clustering
- privacy preserving association rule mining
- nonlinear dimensionality reduction
- dimension reduction
- image processing