Low-rank Riemannian eigensolver for high-dimensional Hamiltonians.
Maxim V. RakhubaAlexander NovikovIvan V. OseledetsPublished in: J. Comput. Phys. (2019)
Keyphrases
- low rank
- high dimensional
- high dimensional data
- manifold learning
- low dimensional
- dimensionality reduction
- matrix completion
- linear combination
- convex optimization
- low rank matrix
- singular value decomposition
- missing data
- matrix factorization
- high dimensionality
- semi supervised
- kernel matrix
- non rigid structure from motion
- data points
- matrix decomposition
- similarity search
- high order
- rank minimization
- feature space
- minimization problems
- input space
- data sets
- nearest neighbor
- singular values
- robust principal component analysis
- euclidean space
- trace norm
- sparse coding
- geodesic distance
- riemannian manifolds
- parameter space
- missing values
- least squares
- machine learning