用少量标注数据连接无配对输入输出的聚类，实现高效低样本预测。
Bridged Clustering: Semi-Supervised Sparse Bridging
- 独立聚类输入输出数据，再用少量配对样本构建稀疏桥接
- 在低监督下性能接近顶尖方法，且仅需极少标注样本
- 适合缺乏成对数据但有大量单模态数据的场景
我们提出Bridged Clustering，一种半监督框架，可从任意无配对的输入X和输出Y数据集学习预测器。方法先独立对X和Y进行聚类，再仅用少量配对样本学习一个稀疏且可解释的簇间桥接关系。推理时，新输入x被分配到最近的输入簇，对应输出簇的质心即为预测结果ŷ。与传统半监督学习不同，该方法显式利用仅含输出的数据；与密集传输类方法相比，保持了稀疏可解释的对齐。理论分析表明，在聚类错误率和桥接错误率有界条件下，算法能成为高效有效的预测器。实验显示，本方法在低监督设置下性能媲美最先进方法，同时保持简单、模型无关和高度标签效率。
原文摘要 · Abstract (English)
We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction $\hat{y}$. Unlike traditional SSL, Bridged Clustering explicitly leverages output-only data, and unlike dense transport-based methods, it maintains a sparse and interpretable alignment. Through theoretical analysis, we show that with bounded mis-clustering and mis-bridging rates, our algorithm becomes an effective and efficient predictor. Empirically, our method is competitive with SOTA methods while remaining simple, model-agnostic, and highly label-efficient in low-supervision settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。