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ULDC: uncertainty-based learning for deep clustering
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DOI：10.1007/s10489-024-06125-2.png)
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Deep clustering has gained prominence due to its impressive capability to handle high-dimensional real-world data. However, in the absence of ground-truth labels, existing clustering methods struggle to discern false positives that resemble the target cluster and false negatives that visually differ but maintain semantic consistency. The unreliable projections caused by visual ambiguity disrupt representation learning, leading to sub-optimal clustering outcomes. To address this challenge, we propose a novel method called uncertainty-based learning for deep clustering (ULDC), which aims to discover more optimal cluster structures within data from an uncertainty perspective. Specifically, we utilize the Dirichlet distribution to quantify the uncertainty of feature projections in the latent space, providing a probabilistic framework for modeling uncertainty during the clustering process. We then develop uncertainty-based learning to mitigate the interference caused by false positives and negatives in the clustering tasks. Additionally, a semantic calibration module is introduced to achieve a global alignment of cross-instance semantics, facilitating the learning of clustering-favorite representations. Extensive experiments on five widely-used benchmarks demonstrate the effectiveness of ULDC. The source code is available from https://github.com/YL616/ULDC.
Keyword:
Unsupervised learning
Representation learning
Deep clustering
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3.5
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7.6K
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