Rethinking k-means from manifold learning perspective.
Quanxue GaoQianqian WangHan LuWei XiaXinbo GaoPublished in: CoRR (2023)
Keyphrases
- manifold learning
- k means
- low dimensional
- semi supervised
- nonlinear dimensionality reduction
- dimensionality reduction
- diffusion maps
- high dimensional
- clustering algorithm
- subspace learning
- high dimensional data
- feature extraction
- dimension reduction
- low dimensional manifolds
- laplacian eigenmaps
- locally linear embedding
- sparse representation
- locality preserving
- clustering method
- manifold learning algorithm
- geodesic distance
- latent space
- training set
- feature space
- head pose estimation
- document clustering
- nonlinear manifold
- discriminant projection
- embedding space
- computer vision
- cluster analysis
- self organizing maps
- multiscale
- image processing