# Bridged Clustering: Semi-Supervised Sparse Bridging

arXiv: https://arxiv.org/abs/2510.07182 · HTML: https://mlsanity.com/paper/2510.07182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants](https://mlsanity.com/paper/1612.02099/) (2016)
- [BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity](https://mlsanity.com/paper/2406.12723/) (2024)
- [Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks](https://mlsanity.com/paper/2302.07579/) (2023)
- [Uncoupled isotonic regression via minimum Wasserstein deconvolution](https://mlsanity.com/paper/1806.10648/) (2018)
- [Learning relationships between data obtained independently](https://mlsanity.com/paper/1601.00504/) (2016)
- [Twin Neural Network Regression is a Semi-Supervised Regression Algorithm](https://mlsanity.com/paper/2106.06124/) (2021)
- [RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier](https://mlsanity.com/paper/2410.22124/) (2024)
- [BOLD v4: A Centralized Bioinformatics Platform for DNA-based Biodiversity Data](https://mlsanity.com/paper/2404.05696/) (2024)
- [Characterization of Hemodynamic Signal by Learning Multi-View Relationships](https://mlsanity.com/paper/1709.05602/) (2017)
- [Linear regression with unmatched data: a deconvolution perspective](https://mlsanity.com/paper/2207.06320/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Found to Designed: Concepts as a Design Axis for Large Language Models](https://mlsanity.com/paper/2607.26825/) (2026)

## Abstract (arXiv)

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 haty. 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.