Name
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. |
Year | Venue | Keywords |
|---|---|---|
2013 | IJCAI | standard gaussian process,gaussian process regression,spatially nonstationary,gaussian process,challenging data property,accurate kalman,latent function value,spatial nonstationary problem,knn-kfgp recursively filter |
Field | DocType | Citations |
Kriging,k-nearest neighbors algorithm,Data set,Extended Kalman filter,Pattern recognition,Computer science,State-space representation,Kalman filter,Gaussian process,Artificial intelligence,Ensemble Kalman filter,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang, Yali | 1 | 91 | 15.18 |
| Chaib-draa, Brahim | 2 | 1190 | 113.23 |