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2017 JMLR JMLR 2017

Kernel Partial Least Squares for Stationary Data

Abstract

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. [abs] [ pdf ][ bib ] © JMLR 2017. (edit, beta)

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — stationary datum
🐣 Hot Topic Early Bird — convergence rate
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Security & Privacy

Authors

Marco Singer , Tatyana Krivobokova , Axel Munk

Topics

Machine Learning > Core Methods > Regression Data Science & Analytics > Methods > Time Series Mathematics & Optimization > Optimization > Continuous Optimization Machine Learning > Core Methods > Dimensionality Reduction

Keywords

nonparametric regression convergence rate stationary datum stationary process kernel partial least square long range dependence
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