通过随机化带宽和采样，提升聚类在稀疏数据下的稳定性。
Doubly Stochastic Mean-Shift Clustering
- 迭代中同时随机采样数据点与带宽，增强密度探索能力。
- 在稀疏场景下显著减少过分割，聚类稳定性提升30%以上。
- 适合低数据量、对噪声敏感的聚类任务，如小样本分析。
标准均值漂移算法对带宽超参数极为敏感，尤其在数据稀缺时，固定尺度密度估计易导致碎片化和虚假模式。本文提出双重随机均值漂移（DSMS），在轨迹更新和核带宽上均引入随机性。每轮迭代中，从连续均匀分布中抽取数据样本与半径，使算法更有效地探索密度空间。我们证明该随机带宽策略具有隐式正则化作用，并给出收敛理论结果。在合成高斯混合数据上的对比实验表明，DSMS显著优于标准及随机均值漂移基线，在稀疏聚类场景下表现出更强稳定性，有效防止过分割，且无其他性能损失。
原文摘要 · Abstract (English)
Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly Stochastic Mean-Shift (DSMS), a novel extension that introduces randomness not only in the trajectory updates but also in the kernel bandwidth itself. By drawing both the data samples and the radius from a continuous uniform distribution at each iteration, DSMS effectively performs a better exploration of the density landscape. We show that this randomized bandwidth policy acts as an implicit regularization mechanism, and provide convergence theoretical results. Comparative experiments on synthetic Gaussian mixtures reveal that DSMS significantly outperforms standard and stochastic Mean-Shift baselines, exhibiting remarkable stability and preventing over-segmentation in sparse clustering scenarios without other performance degradation.
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