StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm

TL;DR

StreaMRAK introduces a streaming, multi-resolution kernel ridge regression algorithm that efficiently handles large datasets by reducing memory and computation through adaptive sub-sampling, enabling real-time, accurate predictions.

Contribution

It presents a novel streaming KRR method with multi-resolution refinement and adaptive sub-sampling to manage large data efficiently.

Findings

Fast and accurate predictions on synthetic and real data.

Reduced memory and computational complexity.

Effective continual refinement of predictions.

Abstract

Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data size far exceeds the memory size. Such applications are increasingly common in data mining, bioinformatics, and control. A powerful paradigm for computing on data sets that are too large for memory is the streaming model of computation, where we process one data sample at a time, discarding each sample before moving on to the next one. In this paper, we propose StreaMRAK - a streaming version of KRR. StreaMRAK improves on existing KRR schemes by dividing the problem into several levels of resolution, which allows continual refinement to the predictions. The algorithm reduces the memory requirement by continuously and efficiently integrating new samples into the training model. With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum. The results show that the proposed algorithm is fast and accurate.