Efficient Recursive Implementation of Spatial-Temporal Gaussian Process Regression
作者单位:School of Science and Engineering and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong The Experimental School of Shen Zhen Institute of Advanced Technology
会议名称:《第三十九届中国控制会议》
会议日期:2020年
学科分类:02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学(可授理学、经济学学位)] 070103[理学-概率论与数理统计] 0701[理学-数学]
关 键 词:System identification Spatial-temporal data Big data Gaussian process regression kernel methods Kalman filter and smoother
摘 要:The current implementation of the spatial-temporal Gaussian process regression has computational complexity O(NM),where N and M are the number of temporal and spatial data,respectively,and thus can only be applied to data with large N but relatively small *** this work,we show that by exploring the Kronecker structure in the state-space model realization of the spatial-temporal Gaussian process,we can extend the current implementation with a coordinate transformation and an output transformation(corresponding to data preprocessing),such that the computational complexity is reduced to O(M+NM+NM) and therefore the proposed implementation can be applied to data with large N and moderately large ***,the proposed implementation can be parallelized and the computational complexity can be further lowered if parallel computing is adopted.