Near-optimal Coresets for Robust Clustering
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We consider robust clustering problems in ℝ^d, specifically k-clustering problems (e.g., k-Median and k-Means with m outliers, where the cost for a given center set C ⊂ℝ^d aggregates the distances from C to all but the furthest m data points, instead of all points as in classical clustering. We focus on the ϵ-coreset for robust clustering, a small proxy of the dataset that preserves the clustering cost within ϵ-relative error for all center sets. Our main result is an ϵ-coreset of size O(m + poly(k ϵ^-1)) that can be constructed in near-linear time. This significantly improves previous results, which either suffers an exponential dependence on (m + k) [Feldman and Schulman, SODA'12], or has a weaker bi-criteria guarantee [Huang et al., FOCS'18]. Furthermore, we show this dependence in m is nearly-optimal, and the fact that it is isolated from other factors may be crucial for dealing with large number of outliers. We construct our coresets by adapting to the outlier setting a recent framework [Braverman et al., FOCS'22] which was designed for capacity-constrained clustering, overcoming a new challenge that the participating terms in the cost, particularly the excluded m outlier points, are dependent on the center set C. We validate our coresets on various datasets, and we observe a superior size-accuracy tradeoff compared with popular baselines including uniform sampling and sensitivity sampling. We also achieve a significant speedup of existing approximation algorithms for robust clustering using our coresets.
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