Bootstrap Deep Spectral Clustering with Optimal Transport
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...
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| Vydáno v: | IEEE transactions on multimedia s. 1 - 14 |
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| Jazyk: | angličtina |
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2025
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| ISSN: | 1520-9210, 1941-0077 |
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| 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 <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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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| AbstractList | 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 <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-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 . |
| Author | Rahardja, Susanto Ye, Wei Plant, Claudia Sun, Xin Chen, Chunchun Bohm, Christian Guo, Wengang |
| Author_xml | – sequence: 1 givenname: Wengang surname: Guo fullname: Guo, Wengang email: guowg97@foxmail.com organization: College of Electronic and Information Engineering, Tongji University, Shanghai, China – sequence: 2 givenname: Wei surname: Ye fullname: Ye, Wei email: yew@tongji.edu.cn organization: College of Electronic and Information Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China – sequence: 3 givenname: Chunchun surname: Chen fullname: Chen, Chunchun email: c2chen@tongji.edu.cn organization: Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China – sequence: 4 givenname: Xin surname: Sun fullname: Sun, Xin email: sunxin1984@ieee.org organization: Faculty of Data Science, City University of Macau, Taipa, Macau, China – sequence: 5 givenname: Christian surname: Bohm fullname: Bohm, Christian email: christian.boehm@univie.ac.at organization: Faculty of Computer Science, University of Vienna, Vienna, Austria – sequence: 6 givenname: Claudia surname: Plant fullname: Plant, Claudia email: claudia.plant@univie.ac.at organization: Faculty of Computer Science, University of Vienna, Vienna, Austria – sequence: 7 givenname: Susanto surname: Rahardja fullname: Rahardja, Susanto email: susantorahardja@ieee.org organization: Singapore Institute of Technology, Singapore, Singapore |
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| SubjectTerms | Clustering algorithms Deep clustering Pattern recognition Spectral clustering Unsupervised learning |
| Title | Bootstrap Deep Spectral Clustering with Optimal Transport |
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