A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space.
Vladimir ShenmaierPublished in: LOD (2019)
Keyphrases
- euclidean space
- high dimensional
- low dimensional
- metric space
- data points
- center based clustering
- embedding space
- dimensionality reduction
- vector space
- similarity search
- shape analysis
- euclidean distance
- high dimensional data
- quadratic form
- riemannian manifolds
- geodesic distance
- manifold learning
- dimensional euclidean space
- multi dimensional scaling
- discrete space
- nearest neighbor
- fisher information
- pairwise distances
- parameter space
- distance function
- input space
- constant curvature
- k means
- distance measure
- multidimensional scaling
- lie group
- sparse coding
- data structure
- multi dimensional
- principal component analysis
- pairwise
- pattern recognition