A geometrical connection between sparse and low-rank matrices and its application to manifold learning.
Lawrence K. SaulPublished in: Trans. Mach. Learn. Res. (2022)
Keyphrases
- manifold learning
- low dimensional
- dimensionality reduction
- nonlinear dimensionality reduction
- semi supervised
- dimension reduction
- diffusion maps
- high dimensional
- laplacian eigenmaps
- singular value decomposition
- feature extraction
- subspace learning
- high dimensional data
- head pose estimation
- feature space
- low dimensional manifolds
- manifold learning algorithm
- sparse representation
- geodesic distance
- locality preserving
- neural network
- data sets
- locally linear embedding
- singular values
- face recognition