Joker框架让核方法在大规模数据下内存占用减少90%，还能保持高效准确。
Joker: Joint Optimization Framework for Lightweight Kernel Machines
- 采用双块坐标下降与信任域结合的方法，配合随机特征近似降低内存开销。
- 在多个核模型上实现比现有方法节省90%内存，训练速度和性能相当甚至更优。
- 适合需要在资源受限环境下部署复杂核模型的研究者或工程师使用。
核方法在非线性学习中具有坚实的理论基础，但其可扩展性长期受限。现有方法存在两大问题：(i) 内存开销过高，用户难以承受；(ii) 多数研究集中于核岭回归（KRR），其他模型缺乏探索。本文提出Joker，一个面向多种核模型的联合优化框架，涵盖KRR、逻辑回归和支持向量机。设计了带信任域的双块坐标下降法（DBCD-TR），并引入随机特征的核近似技术，显著降低内存消耗，提升大规模学习效率。实验表明，Joker可节省高达90%内存，在训练时间与性能上达到或超过当前最优方法。
原文摘要 · Abstract (English)
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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