@article{Gribel2021,
abstract = {Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire. This work presents a clustering model that incorporates pairwise annotations in the form of must-link and cannot-link relations and considers possible annotation inaccuracies (i.e., a common setting when experts provide pairwise supervision). We propose a generative model that assumes Gaussian-distributed data samples along with must-link and cannot-link relations generated by stochastic block models. We adopt a maximum-likelihood approach and demonstrate that, even when supervision is weak and inaccurate, accounting for relational information significantly improves clustering performance. Relational information also helps to detect meaningful groups in real-world datasets that do not fit the original data-distribution assumptions. Additionally, we extend the model to integrate prior knowledge of experts' accuracy and discuss circumstances in which the use of this knowledge is beneficial.},
archivePrefix = {arXiv},
arxivId = {2104.02146},
author = {Gribel, D. and Gendreau, M. and Vidal, T.},
eprint = {2104.02146},
file = {:C$\backslash$:/Users/Thibaut/Documents/Mendeley-Articles/Gribel, Gendreau, Vidal/Gribel, Gendreau, Vidal - 2022 - Semi-supervised clustering with inaccurate pairwise annotations.pdf:pdf},
journal = {Information Sciences},
number = {C},
pages = {441--457},
title = {{Semi-supervised clustering with inaccurate pairwise annotations}},
url = {https://arxiv.org/pdf/2104.02146.pdf},
volume = {607},
year = {2022}
}

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