DeepAI AI Chat
Log In Sign Up

Distributed filtered hyperinterpolation for noisy data on the sphere

10/06/2019
by   Shao-Bo Lin, et al.
0

Problems in astrophysics, space weather research and geophysics usually need to analyze noisy big data on the sphere. This paper develops distributed filtered hyperinterpolation for noisy data on the sphere, which assigns the data fitting task to multiple servers to find a good approximation of the mapping of input and output data. For each server, the approximation is a filtered hyperinterpolation on the sphere by a small proportion of quadrature nodes. The distributed strategy allows parallel computing for data processing and model selection and thus reduces computational cost for each server while preserves the approximation capability compared to the filtered hyperinterpolation. We prove quantitative relation between the approximation capability of distributed filtered hyperinterpolation and the numbers of input data and servers. Numerical examples show the efficiency and accuracy of the proposed method.

READ FULL TEXT
07/18/2020

Distributed Learning via Filtered Hyperinterpolation on Manifolds

Learning mappings of data on manifolds is an important topic in contempo...
04/10/2019

Analyzes of the Distributed System Load with Multifractal Input Data Flows

The paper proposes a solution an actual scientific problem related to lo...
06/13/2022

Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models

Distributed deep learning is becoming increasingly popular due to the ex...
06/22/2018

Removing the Curse of Superefficiency: an Effective Strategy For Distributed Computing in Isotonic Regression

We propose a strategy for computing the isotonic least-squares estimate ...
07/26/2017

Rational Points on the Unit Sphere: Approximation Complexity and Practical Constructions

Each non-zero point in R^d identifies a closest point x on the unit sphe...
02/26/2018

An algorithm for computing Fréchet means on the sphere

For most optimisation methods an essential assumption is the vector spac...