Kernel Partial Least Squares for Stationary Data

被引：0

作者：

Singer, Marco ^{[1
]}

Krivobokova, Tatyana ^{[1
]}

Munk, Axel ^{[1
]}

机构：

[1] Georg August Univ, Inst Math Stochast, D-37077 Gottingen, Germany

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2017年 / 18卷

关键词：

effective dimensionality; long range dependence; nonparametric regression; source condition; protein dynamics; REGRESSION; RATES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We consider the kernel partial least squares algorithm for non-parametric regression with stationary dependent data. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a source and an effective dimensionality condition. It is shown both theoretically and in simulations that long range dependence results in slower convergence rates. A protein dynamics example shows high predictive power of kernel partial least squares.

引用

页数：41

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