Continuous-Time DC Kernel-A Stable Generalized First-Order Spline Kernel

被引:21
|
作者
Chen, Tianshi [1 ,2 ,3 ]
机构
[1] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China
[2] Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
[3] Linkoping Univ, Dept Elect Engn, Linkoping, Sweden
基金
中国国家自然科学基金; 瑞典研究理事会;
关键词
Kernal; MaxEnt; orthonormal basis expansion; regularization methods; system identification; tridiagonal inverse; LINEAR-SYSTEM IDENTIFICATION;
D O I
10.1109/TAC.2018.2825365
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The stable spline (SS) kernel and the diagonal correlated (DC) kernel are two kernels that have been applied and studied extensively for kernel-based regularized LTI system identification. In this note, we show that similar to the derivation of the SS kernel, the continuous-time DC kernel can be derived by applying the same "stable" coordinate change to a "generalized" first-order spline kernel, and thus, can be interpreted as a stable generalized first-order spline kernel. This interpretation provides new facets to understand the properties of the DC kernel. In particular, we derive a new orthonormal basis expansion of the DC kernel and the explicit expression of the norm of the reproducing kernel Hilbert space associated with the DC kernel. Moreover, for the nonuniformly sampled DC kernel, we derive its maximum entropy property and show that its kernel matrix has tridiagonal inverse.
引用
收藏
页码:4442 / 4447
页数:6
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