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.

本文介绍了一种基于核的分类器，通过将核矩阵视为广义图，并利用图嵌入技术的最新进展，提出了一种新的核矩阵嵌入方法，能够快速且可扩展地进行嵌入，无缝地整合多个核以增强学习过程。通过随机变量，我们对该方法进行了人群级别的理论分析。在实证方面，我们的方法在各种模拟和真实数据集上表现出优越的运行时间，与支持向量机和两层神经网络等标准方法相比，实现了可比较的分类准确性。