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ConstraintMatch for Semi-constrained Clustering by Goschenhofer, Jann; Bischl, Bernd; Kira, Zsolt is a scholarly article available to read on EtoBox.

What is ConstraintMatch for Semi-constrained Clustering about?

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

Author

Goschenhofer, Jann; Bischl, Bernd; Kira, Zsolt

Published

2023

Language

EN