Low-Rank Matrix Approximations Do Not Need a Singular Value Gap.
Petros DrineasIlse C. F. IpsenPublished in: SIAM J. Matrix Anal. Appl. (2019)
Keyphrases
- singular values
- low rank matrix
- low rank
- singular value decomposition
- matrix decomposition
- convex optimization
- linear combination
- matrix factorization
- approximation methods
- missing data
- kernel matrix
- semi supervised
- least squares
- dimensionality reduction
- high order
- high dimensional data
- higher order
- collaborative filtering
- data sets
- moving objects
- image processing