Real-Time Regression with Dividing Local Gaussian Processes

TL;DR

This paper introduces dividing local Gaussian processes, a new scalable approach for real-time regression that divides the input space iteratively, achieving sublinear complexity and high accuracy for large datasets.

Contribution

The paper presents a novel dividing local Gaussian process method that improves computational efficiency and predictive performance in real-time regression tasks.

Findings

Achieves sublinear computational complexity in training data size.

Provides accurate predictive distributions with fast prediction and update speeds.

Outperforms state-of-the-art methods on real-world datasets.

Abstract

The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models. While exact Gaussian process regression shows various favorable theoretical properties (uncertainty estimate, unlimited expressive power), the poor scaling with respect to the training set size prohibits its application in big data regimes in real-time. Therefore, this paper proposes dividing local Gaussian processes, which are a novel, computationally efficient modeling approach based on Gaussian process regression. Due to an iterative, data-driven division of the input space, they achieve a sublinear computational complexity in the total number of training points in practice, while providing excellent predictive distributions. A numerical evaluation on real-world data sets shows their advantages over other state-of-the-art methods in terms of accuracy as well as prediction and update speed.