用近邻图加速数据集精简，训练更快更省内存。
Efficient Coreset Selection via K-Nearest Neighbor Graphs
- 基于K近邻图构建局部重要性评估，避免存储全距离矩阵。
- 在4个真实数据集上精度相当，选集时间减少2.3至41.2倍。
- 适合大规模数据训练，内存占用降至基线的0.3%~7.5%。
核心集选择通过用小而具代表性的子集替代大规模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，但其选择阶段常依赖密集的成对距离或大型项-簇边界矩阵，导致在大规模数据集上出现高时间和内存开销。本文提出一种基于K近邻图的轻量级核⼼集选择方法——KNNG-CS。该方法利用局部邻域结构估算每个数据项的重要性，并在不维护二次距离矩阵的前提下贪婪地选取代表性节点，仅需线性存储于边数。在四个真实世界数据集上的实验表明，KNNG-CS在精度上与主流梯度近似核⼼集方法相当，同时将选择时间减少2.3×至41.2×，峰值内存降至基线的0.3%至7.5%。
原文摘要 · Abstract (English)
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.
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