---
title: "A KNN Based Kalman Filter Gaussian Process Regression"
authors:
  - "Yali Wang"
  - "Brahim Chaib-draa"
doi: ""
url: "https://ijcai.org/proceedings/2013/262"
pdf: "https://ijcai.org/proceedings/2013/0262.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1771-1776"
track: ""
keywords:
---

# A KNN Based Kalman Filter Gaussian Process Regression

Yali Wang, Brahim Chaib-draa

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1771-1776.

## Abstract

The standard Gaussian process (GP) regression is often intractable when a data set is large or spatially nonstationary. In this paper, we address these challenging data properties by designing a novel K nearest neighbor based Kalman filter Gaussian process (KNN-KFGP) regression. Based on a state space model established by the KNN driven data grouping, our KNN-KFGP recursively filters out the latent function values in a computationally efficient and accurate Kalman filtering framework. Moreover, KNN allows each test point to find its strongly correlated local training subset, so our KNN-KFGP provides a suitable way to deal with spatial nonstationary problems. We evaluate the performance of our KNN-KFGP on several synthetic and real data sets to show its validity.