Robust Gaussian process with iterative trimming. The main idea of ITGP is to iteratively trim a proportion of the points with the largest absolute residuals, so that the remaining sample can better describe the bulk pattern of the data.
Nov 22, 2020 · Abstract:The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers. This paper presents a new ...
ITGP is a new robust GP regression algorithm that iteratively trims the most extreme data points.
The trimmed marginal likelihood is particularly attractive for Gaussian process (GP) regression where the marginal likelihood has an analytic solution. In ...
Nov 24, 2020 · This paper presents a new robust GP regression algorithm that iteratively trims the most extreme data points. While the new algorithm retains ...
A novel extension to the GP framework is presented using a contaminated normal likelihood function to better account for heteroscedastic variance and ...
Robust Gaussian Process with Iterative Trimming. Contribute to syrte/robustgp development by creating an account on GitHub.
People also ask
What is the Gaussian process based regression?
What are the assumptions of the Gaussian process regression?
What are the advantages and disadvantages of Gaussian process regression?
What is the Gaussian process model?
Nov 22, 2020 · The model prediction of the Gaussian process (GP) regression can be significantly biased when the data are contaminated by outliers. We propose ...
May 6, 2024 · We propose an improved framework based on Gaussian process regression for general transformed coordinates, which has improved efficiency and ...
Jul 31, 2023 · We propose a new method that identifies outliers by finding a subset of data points T such that the marginal likelihood of all remaining data ...