Geometry and statistics-preserving manifold embedding for nonlinear dimensionality reduction.
Md Tauhidul IslamLei XingPublished in: Pattern Recognit. Lett. (2021)
Keyphrases
- nonlinear dimensionality reduction
- manifold embedding
- manifold learning
- low dimensional manifolds
- low dimensional
- dimensionality reduction
- laplacian eigenmaps
- geodesic distance
- dimension reduction
- high dimensional data
- high dimensional
- feature mapping
- embedding space
- manifold structure
- locally linear embedding
- semi supervised
- feature extraction
- three dimensional
- riemannian manifolds
- data sets
- principal component analysis
- pattern recognition
- cluster analysis
- subspace learning
- dimensionality reduction methods
- vector space
- sparse representation
- unsupervised learning
- intrinsic dimensionality
- feature space
- data analysis