Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913735980875776 |
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| author | Jin, Jiaqi Wang, Siwei Dong, Zhibin Yang, Xihong Liu, Xinwang Zhu, En He, Kunlun |
| author_facet | Jin, Jiaqi Wang, Siwei Dong, Zhibin Yang, Xihong Liu, Xinwang Zhu, En He, Kunlun |
| contents | Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors like privacy concerns and device malfunctions. A key challenge is effectively utilizing available instances to recover missing views. Existing methods frequently overlook the heterogeneity among views during recovery, leading to significant distribution discrepancies between recovered and true data. Additionally, many approaches focus on cross-view correlations, neglecting insights from intra-view reliable structure and cross-view clustering structure. To address these issues, we propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency. Extensive experiments on benchmarks demonstrate the superiority of BURG in the incomplete multi-view scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance Jin, Jiaqi Wang, Siwei Dong, Zhibin Yang, Xihong Liu, Xinwang Zhu, En He, Kunlun Computer Vision and Pattern Recognition Machine Learning Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors like privacy concerns and device malfunctions. A key challenge is effectively utilizing available instances to recover missing views. Existing methods frequently overlook the heterogeneity among views during recovery, leading to significant distribution discrepancies between recovered and true data. Additionally, many approaches focus on cross-view correlations, neglecting insights from intra-view reliable structure and cross-view clustering structure. To address these issues, we propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency. Extensive experiments on benchmarks demonstrate the superiority of BURG in the incomplete multi-view scenario. |
| title | Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.11017 |