Convergence Rates of Spectral Regularization Methods: A Comparison between Ill-Posed Inverse Problems and Statistical Kernel Learning.
Sabrina GuastavinoFederico BenvenutoPublished in: SIAM J. Numer. Anal. (2020)
Keyphrases
- inverse problems
- regularization methods
- convergence rate
- kernel learning
- image reconstruction
- global optimization
- convex optimization
- regularization method
- optimization problems
- optimization methods
- kernel function
- early vision
- kernel methods
- partial differential equations
- multiple kernel learning
- kernel matrix
- smoothness constraint
- particle swarm optimization
- super resolution
- semi supervised
- high resolution
- image processing
- total variation
- learning tasks
- image restoration
- metric learning
- feature vectors
- similarity measure