The Johnson-Lindenstrauss Lemma for Clustering and Subspace Approximation: From Coresets to Dimension Reduction.
Moses CharikarErik WaingartenPublished in: CoRR (2022)
Keyphrases
- johnson lindenstrauss
- dimension reduction
- high dimensional
- high dimensional data
- low dimensional
- high dimensionality
- high dimensional data analysis
- subspace clustering
- data points
- dimensionality reduction
- principal component analysis
- feature subspace
- feature space
- unsupervised learning
- high dimensional problems
- lower dimensional
- cluster analysis
- random projections
- manifold learning
- nearest neighbor
- clustering algorithm
- k means
- feature extraction
- qr decomposition
- discriminative information
- partial least squares
- original data
- clustering method
- linear discriminant analysis
- data clustering
- dimension reduction methods
- subspace learning
- singular value decomposition
- distance metric
- image segmentation
- data analysis
- feature vectors
- semi supervised
- document clustering
- input data