冷启动下高效查询相似度，提升聚类精度
Cold-Start Active Correlation Clustering
- 引入覆盖感知机制，早期促进多样性查询
- 在真实与合成数据上验证方法有效性
- 适合初始无相似度信息的聚类场景
我们研究主动相关聚类问题，即成对相似度未预先提供，需通过主动学习以低成本方式查询。特别关注冷启动场景，此时没有真实的初始成对相似度可用于主动学习。为应对该挑战，我们提出一种覆盖感知方法，旨在早期阶段促进查询多样性。通过多个合成与真实世界实验，证明了该方法的有效性。
原文摘要 · Abstract (English)
We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments.
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