An Online Multiple Kernel Parallelizable Learning Scheme
This work addresses kernel selection issues for machine learning practitioners, offering an incremental improvement through parallelization and multi-kernel integration.
The paper tackles the challenge of kernel selection bias in reproducing kernel Hilbert space methods by proposing a scalable, parallelizable multi-kernel learning scheme that combines single-kernel online methods, and it experimentally shows improved performance in terms of cumulative regularized least squares cost.
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.