{"type":"ScholarlyArticle","title":"Continual Multi-View Clustering with Consistent Anchor Guidance","authors":[{"name":"Chao Zhang","slug":"chao-zhang","given":"Chao","family":"Zhang","affiliation":"Department of Control Science and Intelligence Engineering , Nanjing University"},{"name":"Deng Xu","slug":"deng-xu","given":"Deng","family":"Xu","affiliation":"Department of Control Science and Intelligence Engineering , Nanjing University"},{"name":"Xiuyi Jia","slug":"xiuyi-jia","given":"Xiuyi","family":"Jia","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology"},{"name":"Chunlin Chen","slug":"chunlin-chen","given":"Chunlin","family":"Chen","affiliation":"Department of Control Science and Intelligence Engineering , Nanjing University"},{"name":"Huaxiong Li","slug":"huaxiong-li","given":"Huaxiong","family":"Li","affiliation":"Department of Control Science and Intelligence Engineering , Nanjing University"}],"abstract":"Multi-view clustering (MVC) has recently attracted much attention. Most existing approaches are designed for fixed multi-view data, and cannot deal with the common streaming data in real world. In this paper, we address this problem by proposing a consistent Anchor guided Continual MVC (ACMVC) method in a two-stage way. In initial learning stage, a low-rank anchor graph based model is constructed. In continual learning stage, to leverage the historical knowledge, the multi-level anchor information is reused to refine the model via adding consistency regularization. It not only provides prior knowledge to enhance the exploration on current data, but also captures the similarity relationship between previous and current data, enabling a comprehensive exploitation on streaming data. The proposed model can be optimized efficiently with linear time and space complexity. Experiments demonstrate the effectiveness and efficiency of our method compared with some state-of-the-art approaches.","doi":"10.24963/ijcai.2024/601","url":"https://ijcai.org/proceedings/2024/601","pdf":"https://ijcai.org/proceedings/2024/0601.pdf","markdown":"https://ijcai.org/proceedings/2024/601.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/601","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":601,"pages":{"first":5434,"last":5442},"keywords":["Data Mining: DM: Mining data streams","Machine Learning: ML: Unsupervised learning","Machine Learning: ML: Multi-view learning","Machine Learning: ML: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2022] Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, and Zhao Zhang. 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