{"type":"ScholarlyArticle","title":"A KNN Based Kalman Filter Gaussian Process Regression","authors":[{"name":"Yali Wang","slug":"yali-wang","given":"Yali","family":"Wang","affiliation":""},{"name":"Brahim Chaib-draa","slug":"brahim-chaib-draa","given":"Brahim","family":"Chaib-draa","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/262","pdf":"https://ijcai.org/proceedings/2013/0262.pdf","markdown":"https://ijcai.org/proceedings/2013/262.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/262","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":262,"pages":{"first":1771,"last":1776},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}