文章基本信息

标题：On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation ⁎
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作者：Tianshi Chen ; Martin S. Andersen
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：462-467
DOI：10.1016/j.ifacol.2020.12.222
语种：English
出版社：Elsevier
摘要：AbstractA 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.
关键词：KeywordsSystem identificationkernel-based regularizationsemiseparable kernelskernel designefficient computation