{"type":"ScholarlyArticle","title":"Fair Incomplete Multi-View Clustering via Distribution Alignment","authors":[{"name":"Qianqian Wang","slug":"qianqian-wang","given":"Qianqian","family":"Wang","affiliation":""},{"name":"Haiming Xu","slug":"haiming-xu","given":"Haiming","family":"Xu","affiliation":""},{"name":"Meiling Liu","slug":"meiling-liu","given":"Meiling","family":"Liu","affiliation":""},{"name":"Wei Feng","slug":"wei-feng","given":"Wei","family":"Feng","affiliation":""},{"name":"Xiangdong Zhang","slug":"xiangdong-zhang","given":"Xiangdong","family":"Zhang","affiliation":""}],"abstract":"Incomplete multi-view clustering (IMVC) extracts consistent and complementary information from multi-source/modality data with missing views, aiming to partition the data into different clusters. It can effectively address the problem of unsupervised multi-source data analysis in complex environments and has gained considerable attention. However, the fairness of IMVC remains underexplored, particularly when data contains sensitive features ({e.g.}, gender, marital status, and age). To tackle the problem, this work presents a novel Fair Incomplete Multi-View Clustering (FIMVC) method. The proposed FIMVC introduces fairness constraints to ensure clustering results are independent of sensitive features. Additionally, it learns consensus representations to enhance clustering performance by maximizing mutual information and aligning the distributions of different views. Experimental results on three datasets containing sensitive features demonstrate that our method improves the fairness of clustering results while outperforming state-of-the-art IMVC methods in clustering performance.","doi":"10.24963/ijcai.2025/710","url":"https://ijcai.org/proceedings/2025/710","pdf":"https://ijcai.org/proceedings/2025/0710.pdf","markdown":"https://ijcai.org/proceedings/2025/710.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/710","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":710,"pages":{"first":6379,"last":6387},"keywords":["Machine Learning: ML: Clustering","AI Ethics, Trust, Fairness: ETF: Fairness and diversity","Machine Learning: ML: Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}