This paper introduces a new kernel-based classifier by viewing kernel matrices as generalized graphs and leveraging recent progress in graph embedding techniques. The proposed method facilitates fast and scalable kernel matrix embedding, and seamlessly integrates multiple kernels to enhance the learning process. Our theoretical analysis offers a population-level characterization of this approach using random variables. Empirically, our method demonstrates superior running time compared to standard approaches such as support vector machines and two-layer neural network, while achieving comparable classification accuracy across various simulated and real datasets.

翻译：本文通过将核矩阵视为广义图并利用图嵌入技术的最新进展，提出了一种新的基于核的分类器。所提出的方法实现了快速可扩展的核矩阵嵌入，并能无缝集成多个核以增强学习过程。我们的理论分析使用随机变量给出了该方法在总体水平上的特征描述。实证结果表明，与支持向量机和两层神经网络等标准方法相比，我们的方法在多种模拟和真实数据集上实现了相当的分类精度的同时，展现出更优越的运行时间。