AI 中文总结
研究针对质心神经网络训练效率低的问题，提出FastCentNN加速变体。核心方法是引入基于epoch总质心移动的早期分裂策略作训练熵代理，支持不同移动阈值。主要贡献是在保持聚类质量时减少运行时间，为CentNN提供实用高效替代方案。
AI 中文摘要
质心神经网络（CentNN）是一种无监督竞争学习算法，其中质心分裂仅在严格的局部稳定后触发，这通常会导致在模型扩展之前出现长时间的低移动训练阶段。本报告提出了FastCentNN，这是一种加速变体，通过引入基于每个epoch的总质心移动的早期分裂策略来解决这种低效率问题，该策略用作训练熵代理。结果，FastCentNN减少了不必要的重新分配epoch，同时保留了原始的赢家-输家学习动态。FastCentNN支持绝对和阶段相关的移动阈值，允许分裂标准在整个训练过程中保持固定或自适应。在一些基准数据集上的实验表明，FastCentNN始终实现与CentNN相当的聚类质量，同时在合成2D数据集上减少运行时间高达16%，在高维数据集上减少约5%。因此，FastCentNN为CentNN提供了一种实用且高效的替代方案，保留了其在线自适应学习行为，同时通过可配置的分裂阈值提供了简单且可解释的速度-稳定性权衡。
英文摘要
Centroid neural network (CentNN) is an unsupervised competitive learning algorithm in which centroid splitting is triggered only after strict local stabilization, often leading to prolonged low-movement training phases before model expansion. This report proposes FastCentNN, an accelerated variant that addresses this inefficiency by introducing an early splitting strategy based on the total centroid movement per epoch, which serves as a training entropy proxy. As a result, FastCentNN reduces unnecessary reassignment epochs while preserving the original winner-loser learning dynamics. FastCentNN supports both absolute and stage-relative movement thresholds, allowing the splitting criterion to remain either fixed or adaptive throughout training. Experiments on some benchmark datasets show that FastCentNN consistently achieves clustering quality comparable to CentNN while reducing runtime by up to 16% on synthetic 2D datasets and about 5% on high-dimensional datasets. FastCentNN therefore provides a practical and efficient drop-in replacement for CentNN, retaining its online adaptive learning behavior while offering a simple and interpretable speed-stability trade-off through configurable splitting thresholds.
Comments4 pages, 2 figures
