Joker: Joint Optimization Framework for Lightweight Kernel Machines

Junhong Zhang; Zhihui Lai

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arXiv · 2505.17765

Joker: Joint Optimization Framework for Lightweight Kernel Machines

Junhong Zhang·Zhihui Lai

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Abstract

Kernel methods are powerful tools for nonlinear learning with well-established theory. The scalability issue has been their long-standing challenge. Despite the existing success, there are two limitations in large-scale kernel methods: (i) The memory overhead is too high for users to afford; (ii) existing efforts mainly focus on kernel ridge regression (KRR), while other models lack study. In this paper, we propose Joker, a joint optimization framework for diverse kernel models, including KRR, logistic regression, and support vector machines. We design a dual block coordinate descent method with trust region (DBCD-TR) and adopt kernel approximation with randomized features, leading to low memory costs and high efficiency in large-scale learning. Experiments show that Joker saves up to 90\% memory but achieves comparable training time and performance (or even better) than the state-of-the-art methods.

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Junhong Zhang, Zhihui Lai. 2025-05-23. Joker: Joint Optimization Framework for Lightweight Kernel Machines. https://arxiv.org/abs/2505.17765

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