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» High dimensional polynomial interpolation on sparse grids
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ICCS
2003
Springer
14 years 20 days ago
Parallelisation of Sparse Grids for Large Scale Data Analysis
Sparse Grids are the basis for efficient high dimensional approximation and have recently been applied successfully to predictive modelling. They are spanned by a collection of si...
Jochen Garcke, Markus Hegland, Ole Møller N...
SIAMNUM
2010
120views more  SIAMNUM 2010»
13 years 2 months ago
Sparse Spectral Approximations of High-Dimensional Problems Based on Hyperbolic Cross
Hyperbolic cross approximations by some classical orthogonal polynomials/functions in both bounded and unbounded domains are considered in this paper. Optimal error estimates in pr...
Jie Shen, Li-lian Wang
ICASSP
2010
IEEE
13 years 7 months ago
Robust regression using sparse learning for high dimensional parameter estimation problems
Algorithms such as Least Median of Squares (LMedS) and Random Sample Consensus (RANSAC) have been very successful for low-dimensional robust regression problems. However, the comb...
Kaushik Mitra, Ashok Veeraraghavan, Rama Chellappa
LSSC
2001
Springer
13 years 12 months ago
On the Parallelization of the Sparse Grid Approach for Data Mining
Abstract. Recently we presented a new approach [5, 6] to the classification problem arising in data mining. It is based on the regularization network approach, but in contrast to ...
Jochen Garcke, Michael Griebel
SIP
2003
13 years 8 months ago
Polyphase Antialiasing in Enlargements
Changing resolution of images is a common operation. It is also common to use simple, i.e., small interpolation kernels satisfying some ”smoothness” qualities that are determi...
Daniel Seidner