On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Chen, Tianshi; Andersen, Martin S.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 462
• Page End: 467
• Publication Date: 2020
• Subjects: System identification -- kernel-based regularization -- semiseparable kernels -- kernel design -- efficient computation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.222
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A long-standing problem for kernel-based regularization methods is their high computational complexity O(N 3 ), where N is the number of data points. In this paper, we show that for semiseparable kernels and some typical input signals, their computational complexity can be lowered to O(Nq 2 ), where q is the output kernel's semiseparability rank that only depends on the chosen kernel and the input signal.