{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-multi-view-subspace-clustering-for","title":"Adaptive multi-view subspace clustering for high-dimensional data,","arxiv_id":null,"date":"2020-02-01","proceeding":"Pattern Recognition Letters 2020 2","authors":["Yan Fei ; Wang Xiaodong ; Zeng Zhiqiang ; Hong Chaoqun"],"abstract":"With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims to determine the distribution of views by extra empirical parameters and search the optimal projection matrix by eigenvalue decomposition, which is impractical for real-world applications. In this paper, we propose a new adaptive multi-view subspace clustering method to integrate heterogenous data in the low-dimensional feature space. Concretely, we extend K-means clustering with feature learning to handle high-dimensional data. Besides, for multi-view data, we evaluate the weights of distinct views according to their compactness of the cluster structure in the low-dimensional subspace. We apply the proposed method to four benchmark datasets and compare it with several widely used clustering algorithms. Experimental results demonstrate the effectiveness of the proposed method.","url_abs":"https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract","url_pdf":"https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-multi-view-subspace-clustering-for","repo_url":"https://github.com/x-d-wang/x-d-wang.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"multi-view-subspace-clustering","task_name":"Multi-view Subspace Clustering"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}