对比多种数据压缩方法，为在线高斯过程提供高效选型指导
Comparison of Data Reduction Criteria for Online Gaussian Processes
- 统一比较多种数据压缩准则的计算开销与压缩效果
- 在真实系统辨识任务中验证方法有效性，支持动态数据流处理
- 提出新筛选机制，适合实时系统建模与资源受限场景
高斯过程（GPs）因灵活建模和不确定性量化能力被广泛用于回归与系统辨识，但其计算复杂度限制了在大数据集上的应用。在流式数据场景下，数据不断累积，即使稀疏高斯过程也难以为继。在线高斯过程通过设定数据点上限并移除冗余点来缓解此问题。本文对多种数据压缩准则进行了统一比较，分析其计算复杂度与压缩行为。在基准函数与真实世界数据集（包括动态系统辨识任务）上进行评估，并提出新的接受准则以进一步剔除冗余数据点。研究结果为在线高斯过程算法选择合适压缩准则提供了实用指导。
原文摘要 · Abstract (English)
Gaussian Processes (GPs) are widely used for regression and system identification due to their flexibility and ability to quantify uncertainty. However, their computational complexity limits their applicability to small datasets. Moreover in a streaming scenario, more and more datapoints accumulate which is intractable even for Sparse GPs. Online GPs aim to alleviate this problem by e.g. defining a maximum budget of datapoints and removing redundant datapoints. This work provides a unified comparison of several reduction criteria, analyzing both their computational complexity and reduction behavior. The criteria are evaluated on benchmark functions and real-world datasets, including dynamic system identification tasks. Additionally, acceptance criteria are proposed to further filter out redundant datapoints. This work yields practical guidelines for choosing a suitable criterion for an online GP algorithm.
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