{"type":"ScholarlyArticle","title":"Instance-Aligned Semantic Reconstruction for Incomplete Multi-View Clustering","authors":[{"name":"Weiqing Yan","slug":"weiqing-yan","given":"Weiqing","family":"Yan","affiliation":""},{"name":"Yongteng Du","slug":"yongteng-du","given":"Yongteng","family":"Du","affiliation":""},{"name":"Peng Song","slug":"peng-song","given":"Peng","family":"Song","affiliation":""},{"name":"Chang Tang","slug":"chang-tang","given":"Chang","family":"Tang","affiliation":""}],"abstract":"Incomplete multi-view clustering (IMVC) aims to exploit complementary information from multiple views with missing observations. Recent diffusion-based approaches have shown promise for view completion; however, they often fail to capture instance-aligned global correlations across views and suffer from inefficient inference and loosely coupled optimization. In this paper, we propose IASR, an Instance-Aligned Semantic Reconstruction framework for IMVC. IASR formulates missing-view recovery as a cross-view token alignment generator, in which noisy targets, observed views, and timestep embeddings are jointly represented as tokens and interact to capture long-range cross-view dependencies throughout the denoising trajectory. To stabilize representation learning under generative noise, we further introduce a stable–active dual-encoder representation architecture with a generative-adaptive contrastive learning strategy that tightly couples view completion and clustering. Extensive experiments on eight benchmark datasets demonstrate that IASR consistently outperforms state-of-the-art IMVC methods, especially under high missing-rate settings, while achieving improved inference efficiency.","doi":"10.24963/ijcai.2026/572","url":"https://ijcai.org/proceedings/2026/572","pdf":"https://ijcai.org/proceedings/2026/0572.pdf","markdown":"https://ijcai.org/proceedings/2026/572.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/572","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":572,"pages":{"first":5137,"last":5145},"keywords":["Machine Learning: Clustering","Machine Learning: Multi-view learning","Machine Learning: Deep learning architectures"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}