[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85218-en":3,"doc-seo-85218-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85218,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering","Prototype-based incomplete multi-view clustering leverages prototypes as semantic anchors for missing-view imputation, yet prior work is constrained by three issues: it overemphasizes cross-view prototype consistency while neglecting view-specific cues; it relies mainly on instance-level contrast that does not retain cluster-level relational structure; and it performs imputation with global prototypes only, ignoring local geometric neighborhoods. SPORT decouples orthogonal shared and view-specific prototype components, adds structure-aware contrastive learning to model cluster relations, and combines global and local neighborhood matching for robust recovery.","SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering  \nYaoyuan Guo, Zhibin Gu, Songhe Feng, Yuhui Zheng, Bing Li  \narXiv :2607 . 10413v1 [ cs .CV] 11 Jul 2026  \nAbstract—Prototype-based Incomplete Multi-view Clustering has recently attracted increasing attention by exploiting prototypesas semantic anchors for missing-view imputation. However, existing approaches are still limited in three aspects. First, they typically focus on enforcing cross-view prototype consistency, while ignoring view-specific information embedded in prototypes, thus limiting multi-view expressiveness. Second, most methods rely on instance-level contrastive learning that only aligns paired samples across views, failing to preserve cluster-level relational structures. Third, missing-view imputation is usually performed using global prototypes alone, without considering local geometric neighborhood structures, leading to inaccurate recovery of missing representations. To address these limitations, we propose a novel framework termed Structure-aware PrOtotype disentanglement foR incomplete multi-view clusTering (SPORT), which explicitly disentangles shared and view-specific components of prototypes while preserving cluster-level relational structures. Specifically, we decouple prototypes into orthogonal shared and view-specific components, aligning only shared components to capture consensus semantics while decorrelating view-specific components to preserve complementary information. Meanwhile, a structureaware contrastive learning mechanism is incorporated to explicitly model cluster-level relationships during cross-view representation learning. Furthermore, a hybrid imputation strategy integrates global prototype matching with local neighborhood matching, enabling joint exploitation of semantic prototypes and manifold structures for missing-view recovery. Extensive experiments on six benchmark datasets show that SPORT achieves superior performance over state-of-the-art methods under various missing rates.  \nIndex Terms—Incomplete multi-view clustering, prototype disentanglement, structure-aware contrastive learning, missingview imputation  \nI. INTRODUCTION  \nWithThe rapid development of knowledge discovery  \nand data mining techniques, data are increasingly collected and stored in multiple modalities. Such multi-source  \nY. Guo is with the College of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang 050024, China, and also with the Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China. (e-mail: [12312902@mail.sustech.edu.cn](12312902@mail.sustech.edu.cn)) .  \nZ. Gu is with the College of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang 050024, China, and also with the Key Laboratory of Tibetan Information Processing, Ministry of Education, Qinghai Normal University, Xining 810008, China. (e-mail: [guzhibin@hebtu.edu.cn](guzhibin@hebtu.edu.cn)).  \nS. Feng is with the School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China. (e-mail: [shfeng@bjtu.edu.cn](shfeng@bjtu.edu.cn)).  \nY. Zheng is with the Key Laboratory of Tibetan Information Processing, Ministry of Education, Qinghai Normal University, Xining 810008, China.(e-mail: [zhengyh@vip.126.com](zhengyh@vip.126.com)).  \nB. Li is with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. (e-mail: [bli@nlpr.ia.ac.cn](bli@nlpr.ia.ac.cn)).  \nCorresponding author: Zhibin Gu.  \ndata are referred to as multi-view data. Multi-View Learning (MVL), which aims to leverage the shared (common) and complementary information across different views to enhance the performance of various learning tasks, has been extensively studied [1–4] . As a representative unsupervised paradigm within MVL, Multi-View Clustering (MVC) seeks to partition unlabeled multi-view","cbCaipOh1MXHJcxc","https://ap.wps.com/l/cbCaipOh1MXHJcxc","pdf",9344904,2,1,16,"English","en",105,"# INTRODUCTION\n# RELATED WORK\n## Traditional IMVC Methods\n## Deep Learning-Based IMVC Methods\n## Prototype-Based IMVC Methods\n# PROPOSED METHOD OVERVIEW","[{\"question\":\"What problems does SPORT address in prototype-based incomplete multi-view clustering?\",\"answer\":\"SPORT addresses limitations in cross-view consistency enforcement, lack of cluster-level relational preservation, and inaccurate missing-view recovery caused by using only global prototypes without local neighborhood geometry.\"},{\"question\":\"How does SPORT represent prototypes to improve multi-view expressiveness?\",\"answer\":\"SPORT explicitly disentangles prototypes into orthogonal shared components and view-specific components, aligning shared parts across views while decorrelating view-specific components to keep complementary information.\"},{\"question\":\"How does SPORT perform missing-view imputation?\",\"answer\":\"SPORT uses a hybrid imputation strategy that integrates global prototype matching with local neighborhood matching, jointly exploiting semantic prototypes and manifold structures for recovery.\"}]",1784201807,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"sport-structure-aware-prototype-disentanglement-for-incomplete-multi-view-clustering","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/sport-structure-aware-prototype-disentanglement-for-incomplete-multi-view-clustering/85218/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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