{"type":"ScholarlyArticle","title":"Active Deep Multi-view Clustering","authors":[{"name":"Helin Zhao","slug":"helin-zhao","given":"Helin","family":"Zhao","affiliation":"Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging ,"},{"name":"Wei Chen","slug":"wei-chen","given":"Wei","family":"Chen","affiliation":"Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging ,; School of Computer Science and Technology , Anhui University"},{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":"Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging ,"}],"abstract":"Deep multi-view clustering has been widely studied. However, since it is an unsupervised task, where no labels are used to guide the training, it is still unreliable especially when handling complicated data. Although deep semi-supervised multi-view clustering can alleviate this problem by using some supervised information, the supervised information is often pregiven or randomly selected. Unfortunately, as we know, the clustering performance highly depends on the quality of the supervised information and most of the semi-supervised methods ignore the supervised information selection. To tackle this problem, in this paper, we propose a novel active deep multi-view clustering method, which can actively select important data for querying human annotations. In this method, we carefully design a fusion module, an active selection module, a supervised module, and an unsupervised module, and integrate them into a unified framework seamlessly. In this framework, we can obtain a more reliable clustering result with as few annotations as possible. The extensive experiments on benchmark data sets show that our method can outperform state-of-the-art unsupervised and semi-supervised methods, demonstrating the effectiveness and superiority of the proposed method. The code is available at  https://github.com/wodedazhuozi/ADMC .","doi":"10.24963/ijcai.2024/614","url":"https://ijcai.org/proceedings/2024/614","pdf":"https://ijcai.org/proceedings/2024/0614.pdf","markdown":"https://ijcai.org/proceedings/2024/614.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/614","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":614,"pages":{"first":5554,"last":5562},"keywords":["Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Multi-view learning","Machine Learning: ML: Active learning"],"videos":[],"references":[{"position":1,"raw":"[Amini et al., 2009] Massih R Amini, Nicolas Usunier, and Cyril Goutte. 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