Efficient Coreset Selection via K-Nearest Neighbor Graphs

Abstract: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.

Machine Learning (cs.LG)

arXiv:2608.16270 [cs.LG]

(or arXiv:2608.16270v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.16270 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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