{"type":"ScholarlyArticle","title":"Deep Multi-view Subspace Clustering with Anchor Graph","authors":[{"name":"Chenhang Cui","slug":"chenhang-cui","given":"Chenhang","family":"Cui","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Jingyu Pu","slug":"jingyu-pu","given":"Jingyu","family":"Pu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Xiaorong Pu","slug":"xiaorong-pu","given":"Xiaorong","family":"Pu","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu , China; Shenzhen Institute for Advanced Study , University of Electronic Science and Technology of China"},{"name":"Lifang He","slug":"lifang-he","given":"Lifang","family":"He","affiliation":"Department of Computer Science and Engineering , Lehigh University , Bethlehem , USA"}],"abstract":"Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clustering objective is rarely considered in autoencoders, and (2) existing methods typically have a quadratic or even cubic complexity, which makes it challenging to deal with large-scale data. To address these issues, in this paper we propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG). To be specific, DMCAG firstly learns the embedded features for each view independently, which are used to obtain the subspace representations. To significantly reduce the complexity, we construct an anchor graph with small size for each view. Then, spectral clustering is performed on an integrated anchor graph to obtain pseudo-labels. To overcome the negative impact caused by suboptimal embedded features, we use pseudo-labels to refine the embedding process to make it more suitable for the clustering task. Pseudo-labels and embedded features are updated alternately. Furthermore, we design a strategy to keep the consistency of the labels based on contrastive learning to enhance the clustering performance. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods.","doi":"10.24963/ijcai.2023/398","url":"https://ijcai.org/proceedings/2023/398","pdf":"https://ijcai.org/proceedings/2023/0398.pdf","markdown":"https://ijcai.org/proceedings/2023/398.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/398","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":398,"pages":{"first":3577,"last":3585},"keywords":["Machine Learning: ML: Clustering","Machine Learning: ML: Multi-view learning","Machine Learning: ML: Self-supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2012] Xiao Cai, Hua Wang, Heng Huang, and Chris Ding. 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