Work

On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation

Year: 2020

Type: article

Abstract: A long-standing problem for kernel-based regularization methods is their high computational complexity O(N3), 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(Nq2), where q is the output kernel’s semiseparability rank that only depends on the chosen kernel and the input signal.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2020.12.222

Authors: Tianshi Chen, Martin S. Andersen

Institutions: Chinese University of Hong Kong, Shenzhen Research Institute of Big Data, Chinese University of Hong Kong, Shenzhen, Technical University of Denmark

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W3155081622

FWCI: 0.124

Cites: 21

Cited by: 2

Related to: 10