{"type":"ScholarlyArticle","title":"Salient-Residual Decoupled Multi-View Learning for Clustering","authors":[{"name":"Gaokai Wang","slug":"gaokai-wang","given":"Gaokai","family":"Wang","affiliation":""},{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":""},{"name":"Fengyu Zhang","slug":"fengyu-zhang","given":"Fengyu","family":"Zhang","affiliation":""},{"name":"Jie Xu","slug":"jie-xu","given":"Jie","family":"Xu","affiliation":""},{"name":"Chaoning Zhang","slug":"chaoning-zhang","given":"Chaoning","family":"Zhang","affiliation":""},{"name":"Zhen Long","slug":"zhen-long","given":"Zhen","family":"Long","affiliation":""},{"name":"Ce Zhu","slug":"ce-zhu","given":"Ce","family":"Zhu","affiliation":""}],"abstract":"Multi-view clustering aims to utilize information from multiple feature representations to uncover underlying data structures. Most existing methods emphasize learning a consensus representation by enforcing consistency across views. However, those structures that cannot be directly incorporated into the clustering space receive little attention, and are often discarded as noise in practice. In this paper, we argue that such information can be informative signals for the clustering process and should be explicitly modeled rather than suppressed or ignored. Specifically, we propose salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism. The residual subspace can capture the deep-level structure of view information, making positive contributions to the clustering process, under proper constraints. Without assuming stronger view alignment or complementarity, SRDMVC enhances cluster discrimination, avoids spurious consensus, and alleviates representation degradation. Extensive experiments on benchmark datasets demonstrate the superior performance of the method we propose.","doi":"10.24963/ijcai.2026/547","url":"https://ijcai.org/proceedings/2026/547","pdf":"https://ijcai.org/proceedings/2026/0547.pdf","markdown":"https://ijcai.org/proceedings/2026/547.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/547","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":547,"pages":{"first":4912,"last":4920},"keywords":["Machine Learning: Clustering","Machine Learning: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}