Coresets for constrained k-median and k-means clustering in low dimensional Euclidean space.
Melanie SchmidtJulian WargallaPublished in: CoRR (2021)
Keyphrases
- cluster analysis
- euclidean space
- low dimensional
- high dimensional
- k means
- clustering algorithm
- data points
- high dimensional data
- dimensionality reduction
- pairwise distances
- principal component analysis
- riemannian manifolds
- vector space
- manifold learning
- multidimensional scaling
- feature space
- embedding space
- nonlinear dimensionality reduction
- quadratic form
- shape analysis
- euclidean distance
- geodesic distance
- multi dimensional scaling
- discrete space
- fisher information
- dimensional euclidean space
- graph laplacian
- spectral clustering
- manifold structure
- lie group
- metric space
- multi dimensional
- three dimensional
- dissimilarity matrix
- neural network
- data sets