ON THE RELATIONSHIP BETWEEN ONLINE GAUSSIAN PROCESS REGRESSION AND KERNEL LEAST MEAN SQUARES ALGORITHMS

被引：0

作者：

Van Vaerenbergh, Steven ^{[1
]}

Fernandez-Bes, Jesus ^{[2
,3
]}

Elvira, Victor ^{[4
]}

机构：

[1] Univ Cantabria, Dept Commun Engn, Cantabria, Spain

[2] CIBER BBN, Zaragoza, Spain

[3] Univ Zaragoza, IIS Aragon, I3A, BSICoS Grp, Zaragoza, Spain

[4] Univ Carlos III Madrid, Dept Signal Theory & Commun, E-28903 Getafe, Spain

来源：

2016 IEEE 26TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2016年

关键词：

online learning; regression; Gaussian processes; kernel least-mean squares;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences.

引用

页数：6

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