Bootstrap Deep Spectral Clustering with Optimal Transport

• URL: http://arxiv.org/abs/2508.04200v1
• Date: Wed, 06 Aug 2025 08:30:30 GMT
• Title: Bootstrap Deep Spectral Clustering with Optimal Transport
• Authors: Wengang Guo, Wei Ye, Chunchun Chen, Xin Sun, Christian Böhm, Claudia Plant, Susanto Rahardja,
• Abstract summary: Experimental results indicate that BootSC achieves state-of-the-art clustering performance.<n>BootSC accomplishes a notable 16% NMI improvement over the runner-up method on the challenging ImageNet-Dogs dataset.
• Score: 18.630037263667358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these issues, we propose a deep spectral clustering model (named BootSC), which jointly learns all stages of spectral clustering -- affinity matrix construction, spectral embedding, and $k$-means clustering -- using a single network in an end-to-end manner. BootSC leverages effective and efficient optimal-transport-derived supervision to bootstrap the affinity matrix and the cluster assignment matrix. Moreover, a semantically-consistent orthogonal re-parameterization technique is introduced to orthogonalize spectral embeddings, significantly enhancing the discrimination capability. Experimental results indicate that BootSC achieves state-of-the-art clustering performance. For example, it accomplishes a notable 16\% NMI improvement over the runner-up method on the challenging ImageNet-Dogs dataset. Our code is available at https://github.com/spdj2271/BootSC.

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