A new algorithm for high-dimensional uncertainty quantification based on dimension-adaptive sparse grid approximation and reduced basis methods.
Peng ChenAlfio QuarteroniPublished in: J. Comput. Phys. (2015)
Keyphrases
- high dimensional
- noisy data
- computational cost
- dynamic programming
- significant improvement
- preprocessing
- synthetic and real images
- np hard
- conjugate gradient algorithm
- synthetic and real datasets
- search space
- learning algorithm
- theoretical guarantees
- approximation methods
- high dimension
- dimensional data
- sparse data
- objective function
- detection algorithm
- worst case
- nearest neighbor
- k means
- optimal solution
- synthetic datasets
- convergence rate
- segmentation algorithm
- dimensionality reduction
- error tolerance