arXiv cs.AI· Xiaojian Ding, Xian Li, Xiaoying Zhu, Lin Zhao, Kaixiang Wang·· 1 天前AI 评分20
ORCA：面向多视图聚类的正交残差共识对齐方法
ORCA:Orthogonal Residual Consensus Alignment for Multi-View Clustering
AI 导读
研究者提出多视图聚类方法 ORCA（正交残差共识对齐），通过语义增量循环融合模块提取跨视图正交残差作为非冗余增量，并用散度引导门控对循环状态自适应加权。其共识校准分层对比对齐模块以全局共识为视图级、成对及循环表示加权。在五个基准上对比十五种方法，ORCA 在 100leaves 上较最强基线提升 5.25% 准确率，消融实验验证两个模块均有效，代码已开源。
正文 · 原文
Abstract:Deep multi-view clustering often suffers from redundant information accumulation during fusion and indiscriminate alignment across representation levels. We propose Orthogonal Residual Consensus Alignment (ORCA) to address these challenges. Its semantic incremental recurrent fusion module extracts cross-view orthogonal residuals as non-redundant increments and adaptively weights recurrent states through divergence-guided gating. Its consensus-calibrated hierarchical contrastive alignment module uses the global consensus to weight contrastive alignment of view-level, pairwise, and recurrent representations. Together, these modules balance view-specific structure with cross-view consistency. Experiments on five benchmarks against fifteen competing methods demonstrate ORCA's effectiveness, including a 5.25 percent accuracy improvement over the strongest baseline on 100leaves. Ablation studies validate both modules. Code is available at this https URL.
| Comments: | 24 pages,5 figures,2 tables |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16749 [cs.CV] |
| (or arXiv:2611.16749v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16749 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lin Zhao [view email]
[v1]
Thu, 12 Nov 2026 13:00:39 UTC (4,371 KB)
来源：arXiv cs.AI · arxiv.org