Dual-dimensional contrastive learning for incomplete multi-view clustering

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-11-16 DOI:10.1016/j.neucom.2024.128892
Zhengzhong Zhu , Chujun Pu , Xuejie Zhang , Jin Wang , Xiaobing Zhou
{"title":"Dual-dimensional contrastive learning for incomplete multi-view clustering","authors":"Zhengzhong Zhu ,&nbsp;Chujun Pu ,&nbsp;Xuejie Zhang ,&nbsp;Jin Wang ,&nbsp;Xiaobing Zhou","doi":"10.1016/j.neucom.2024.128892","DOIUrl":null,"url":null,"abstract":"<div><div>Incomplete multi-view clustering (IMVC) is a critical task in real-world applications, where missing data in some views can severely limit the ability to leverage complementary information across views. This issue leads to incomplete sample representations, hindering model performance. Current contrastive learning methods for IMVC exacerbate the problem by directly constructing data pairs from incomplete samples, ignoring essential information and resulting in class collisions, where samples from different classes are incorrectly grouped together due to a lack of label guidance. These challenges are particularly detrimental in fields like recommendation systems and bioinformatics, where accurate clustering of high-dimensional and incomplete data is essential for decision-making. To address these issues, we propose Dual-dimensional Contrastive Learning (DCL), an online IMVC model that fills missing values through multi-view consistency transfer, enabling simultaneous clustering and representation learning via instance-level and cluster-level contrastive learning in both row and column spaces. DCL mitigates class collision issues by generating high-confidence pseudo-labels and using an optimal transport matrix, significantly improving clustering accuracy. Extensive experiments demonstrate that DCL achieves state-of-the-art results across five datasets. The code is available at <span><span>https://github.com/2251821381/DCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"615 ","pages":"Article 128892"},"PeriodicalIF":5.5000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224016631","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Incomplete multi-view clustering (IMVC) is a critical task in real-world applications, where missing data in some views can severely limit the ability to leverage complementary information across views. This issue leads to incomplete sample representations, hindering model performance. Current contrastive learning methods for IMVC exacerbate the problem by directly constructing data pairs from incomplete samples, ignoring essential information and resulting in class collisions, where samples from different classes are incorrectly grouped together due to a lack of label guidance. These challenges are particularly detrimental in fields like recommendation systems and bioinformatics, where accurate clustering of high-dimensional and incomplete data is essential for decision-making. To address these issues, we propose Dual-dimensional Contrastive Learning (DCL), an online IMVC model that fills missing values through multi-view consistency transfer, enabling simultaneous clustering and representation learning via instance-level and cluster-level contrastive learning in both row and column spaces. DCL mitigates class collision issues by generating high-confidence pseudo-labels and using an optimal transport matrix, significantly improving clustering accuracy. Extensive experiments demonstrate that DCL achieves state-of-the-art results across five datasets. The code is available at https://github.com/2251821381/DCL.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
不完全多视角聚类的双维对比学习
不完整多视图聚类(IMVC)是现实世界应用中的一项关键任务,因为某些视图中的数据缺失会严重限制利用跨视图互补信息的能力。这一问题会导致样本表示不完整,从而影响模型性能。目前用于 IMVC 的对比学习方法直接从不完整的样本中构建数据对,忽略了基本信息,导致类碰撞,即由于缺乏标签指导,来自不同类的样本被错误地归类在一起,从而加剧了问题的严重性。这些挑战对推荐系统和生物信息学等领域尤为不利,因为在这些领域,对高维和不完整数据进行准确聚类对决策至关重要。为了解决这些问题,我们提出了双维对比学习(Dual-dimensional Contrastive Learning,DCL),这是一种在线 IMVC 模型,它通过多视角一致性转移来填补缺失值,通过行和列空间中的实例级和集群级对比学习,实现同时聚类和表示学习。DCL 通过生成高置信度伪标签和使用最优传输矩阵来缓解类碰撞问题,从而显著提高聚类准确性。大量实验证明,DCL 在五个数据集上取得了最先进的结果。代码可在 https://github.com/2251821381/DCL 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
期刊最新文献
GDoT: A gated dual domain transformer for enhanced MRI off-resonance correction Hybrid safe reinforcement learning: Tackling distribution shift and outliers with the Student-t’s process Editorial Board Single-shot phase-shifting composition fringe projection profilometry by multi-attention fringe restoration network Label-only model inversion attacks: Adaptive boundary exclusion for limited queries
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1