Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging

• URL: http://arxiv.org/abs/2510.07182v2
• Date: Thu, 09 Oct 2025 04:57:59 GMT
• Title: Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
• Abstract summary: We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset.<n>Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples.
• Score: 7.631238459202664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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