Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6928665 | Journal of Computational Physics | 2018 | 19 Pages |
Abstract
We present a sequential method for approximating an unknown function sequentially using random noisy samples. Unlike the traditional function approximation methods, the current method constructs the approximation using one sample at a time. This results in a simple numerical implementation using only vector operations and avoids the need to store the entire data set. The method is thus particularly suitable when data set is exceedingly large. Furthermore, we present a general theoretical framework to define and interpret the method. Both upper and lower bounds of the method are established for the expectation of the results. Numerical examples are provided to verify the theoretical findings.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Yeonjong Shin, Kailiang Wu, Dongbin Xiu,