An Online Multiple Kernel Parallelizable Learning Scheme

TL;DR

This paper introduces a scalable, parallelizable multi-kernel learning scheme that combines multiple online kernel methods to improve performance and reduce kernel selection bias in data-rich tasks.

Contribution

It proposes a novel multi-kernel online learning framework that is parallelizable and applicable to regularized empirical risk minimization, enhancing solution quality.

Findings

Outperforms single-kernel online methods in experiments

Reduces kernel-selection bias in kernel-based learning

Enables parallel computation for efficiency

Abstract

The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.