Fast and Scalable Multi-Kernel Encoder Classifier

2024-06-04 · Cencheng Shen

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.

📄 PDF Abstract BibTeX arXiv:2406.02189

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Tasks

Graph Embedding

Multiple Locally Linear Kernel Machines

2024-01-17 · David Picard

In this paper we propose a new non-linear classifier based on a combination of locally linear classifiers. A well known optimization formulation is given as we cast the problem in a $\ell_1$ Multiple Kernel Learning (MKL…

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Classification General Classification Model Selection One-Class Classification+1

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Classification General Classification One-Class Classification One-class classifier+1

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Classification General Classification Graph Embedding One-Class Classification+3

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2015-03-03 · Andrew Gordon Wilson, Hannes Nickisch

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computati…

Gaussian Processes