Deep Conditional Gaussian Mixture Model for Constrained ClusteringDownload PDF

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: constrained clustering, deep generative models, representation learning
  • TL;DR: We present a novel constrained deep clustering method, namely DC-GMM, that incorporates clustering preferences in the form of pairwise constraints with varying degrees of certainty.
  • Abstract: Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of stochastic gradient variational inference. By explicitly integrating domain knowledge in the form of probabilistic relations, our proposed model (DC-GMM) uncovers the underlying distribution of data conditioned on prior clustering preferences, expressed as \textit{pairwise constraints}. These constraints guide the clustering process towards a desirable partition of the data by indicating which samples should or should not belong to the same cluster. We provide extensive experiments to demonstrate that DC-GMM shows superior clustering performances and robustness compared to state-of-the-art deep constrained clustering methods on a wide range of data sets. We further demonstrate the usefulness of our approach on two challenging real-world applications.
  • Supplementary Material: pdf
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  • Code: https://github.com/lauramanduchi/DC-GMM
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