Fast and Scalable Multi-Kernel Encoder Classifier
This work addresses scalability issues in kernel methods for machine learning practitioners, though it appears incremental as it builds on existing graph embedding techniques.
The paper tackles the problem of slow and non-scalable kernel-based classification by introducing a method that views kernel matrices as graphs and uses graph embedding techniques, resulting in faster running times while maintaining comparable accuracy to standard methods like SVMs and neural networks.
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.