基于K近邻图的高效核心集选择
Efficient Coreset Selection via K-Nearest Neighbor Graphs
- Xidian University（西安电子科技大学）
- Hangzhou Institute, Xidian University（西安电子科技大学杭州研究院）
- The Chinese University of Hong Kong（香港中文大学）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于K近邻图的KNNG-CS核心集选择方法，在保持与梯度近似核心集方法相当精度的同时，大幅降低了选择时间与峰值内存。
AI中文摘要：
核心集选择通过用小型代表性子集替代大型训练集来降低模型训练成本。现有基于梯度近似的核心集方法（如CRAIG及其聚类变体）可保持模型精度，但它们的选择阶段常依赖密集成对距离或大型项-簇边界矩阵，在大型数据集上会导致高时间与内存成本。本文提出KNNG-CS，一种基于K近邻图的轻量级核心集选择方法，该方法利用局部邻域结构估计每个数据项的重要性，贪婪选择代表性节点而无需维护二次距离矩阵，仅需与边数成线性的存储。在四个真实世界数据集上的实验表明，KNNG-CS的精度可与代表性梯度近似核心集方法相当，同时将选择时间降低2.3倍至41.2倍，峰值内存降至基线的0.3%至7.5%。
英文摘要：
Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset. Existing gradient-approximation coreset methods such as CRAIG and cluster-based variants can preserve model accuracy. Still, their selection stages often rely on dense pairwise distances or large item-cluster bound matrices, leading to high time and memory costs on large datasets. This paper proposes KNNG-CS, a lightweight coreset selection method based on a $K$-nearest neighbor graph. KNNG-CS exploits local neighborhood structures to estimate the importance of each data item and greedily selects representative nodes without maintaining a quadratic distance matrix. The method requires only linear storage in the number of edges. Experiments on four real-world datasets show that KNNG-CS achieves accuracy comparable to representative gradient-approximation coreset methods, while reducing selection time by $2.3\times$-$41.2\times$ and peak memory to $0.3\%$-$7.5\%$ of the baselines.
