Constrained clustering allows the training of classification models using
pairwise constraints only, which are weak and relatively easy to mine, while
still yielding full-supervision-level model performance. While they perform
well even in the absence of the true underlying class labels, constrained
clustering models still require large amounts of binary constraint annotations
for training. In this paper, we propose a semi-supervised context whereby a
large amount of \textit{unconstrained} data is available alongside a smaller
set of constraints, and propose \textit{ConstraintMatch} to leverage such
unconstrained data. While a great deal of progress has been made in
semi-supervised learning using full labels, there are a number of challenges
that prevent a naive application of the resulting methods in the
constraint-based label setting. Therefore, we reason about and analyze these
challenges, specifically 1) proposing a \textit{pseudo-constraining} mechanism
to overcome the confirmation bias, a major weakness of pseudo-labeling, 2)
developing new methods for pseudo-labeling towards the selection of
\textit{informative} unconstrained samples, 3) showing that this also allows
the use of pairwise loss functions for the initial and auxiliary losses which
facilitates semi-constrained model training. In extensive experiments, we
demonstrate the effectiveness of ConstraintMatch over relevant baselines in
both the regular clustering and overclustering scenarios on five challenging
benchmarks and provide analyses of its several components.

本文提出了一种半监督背景下的约束聚类模型 ConstraintMatch，通过使用没有约束的数据和伪约束机制解决了伪标签的确认偏差问题，并采用新的伪标签方法选择信息量高的无约束样本，从而实现了半约束模型训练。作者在多项实验中验证了 ConstraintMatch 在常规聚类和过聚类场景中的有效性，并对其多个组成部分进行了分析。