{"title":"关注和学习:通过硬实例感知增强深度多视图集群","authors":"Wenlong Liu , Jiaohua Qin","doi":"10.1016/j.inffus.2025.103724","DOIUrl":null,"url":null,"abstract":"<div><div>Deep contrastive multi-view clustering aims to use contrastive mechanisms to exploit the complementary information from multiple features, which has attracted increasing attention in recent years. However, we observe that most contrastive multi-view clustering methods neglect the false sample pairs caused by hard samples during the process of constructing contrastive sample pairs, including negative samples exhibit high similarity and positive samples exhibit low similarity. To address this problems, we propose a novel deep contrastive multi-view clustering network for hard sample mining, termed <strong>MVC-HSM</strong>. Specifically, we propose a strategy that incorporates both coarse-grained and fine-grained perspectives. At the coarse-grained level, we perform contrastive learning by utilizing prototypes from each view, thereby mitigating hard samples at the sample level. At the fine-grained level, we first construct a comprehensive evaluation function to measure the similarity for the samples based on representation relationships and structures. In combination with the filtering effect of high-confidence pseudo-labels, we further design a contrastive learning loss for hard samples. Thus, the model could automatically increase the weight of hard samples while reducing the weight of easy samples. The superior of MVC-HSM is verified by extensive experiments on public multi-view datasets, demonstrating the proposed MVC-HSM outperforms other state-of-the-art multi-view clustering.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103724"},"PeriodicalIF":15.5000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Focus and learn: boosting deep multi-view clustering via hard instance awareness\",\"authors\":\"Wenlong Liu , Jiaohua Qin\",\"doi\":\"10.1016/j.inffus.2025.103724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Deep contrastive multi-view clustering aims to use contrastive mechanisms to exploit the complementary information from multiple features, which has attracted increasing attention in recent years. However, we observe that most contrastive multi-view clustering methods neglect the false sample pairs caused by hard samples during the process of constructing contrastive sample pairs, including negative samples exhibit high similarity and positive samples exhibit low similarity. To address this problems, we propose a novel deep contrastive multi-view clustering network for hard sample mining, termed <strong>MVC-HSM</strong>. Specifically, we propose a strategy that incorporates both coarse-grained and fine-grained perspectives. At the coarse-grained level, we perform contrastive learning by utilizing prototypes from each view, thereby mitigating hard samples at the sample level. At the fine-grained level, we first construct a comprehensive evaluation function to measure the similarity for the samples based on representation relationships and structures. In combination with the filtering effect of high-confidence pseudo-labels, we further design a contrastive learning loss for hard samples. Thus, the model could automatically increase the weight of hard samples while reducing the weight of easy samples. The superior of MVC-HSM is verified by extensive experiments on public multi-view datasets, demonstrating the proposed MVC-HSM outperforms other state-of-the-art multi-view clustering.</div></div>\",\"PeriodicalId\":50367,\"journal\":{\"name\":\"Information Fusion\",\"volume\":\"127 \",\"pages\":\"Article 103724\"},\"PeriodicalIF\":15.5000,\"publicationDate\":\"2025-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Fusion\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1566253525007869\",\"RegionNum\":1,\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1566253525007869","RegionNum":1,"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}
Focus and learn: boosting deep multi-view clustering via hard instance awareness
Deep contrastive multi-view clustering aims to use contrastive mechanisms to exploit the complementary information from multiple features, which has attracted increasing attention in recent years. However, we observe that most contrastive multi-view clustering methods neglect the false sample pairs caused by hard samples during the process of constructing contrastive sample pairs, including negative samples exhibit high similarity and positive samples exhibit low similarity. To address this problems, we propose a novel deep contrastive multi-view clustering network for hard sample mining, termed MVC-HSM. Specifically, we propose a strategy that incorporates both coarse-grained and fine-grained perspectives. At the coarse-grained level, we perform contrastive learning by utilizing prototypes from each view, thereby mitigating hard samples at the sample level. At the fine-grained level, we first construct a comprehensive evaluation function to measure the similarity for the samples based on representation relationships and structures. In combination with the filtering effect of high-confidence pseudo-labels, we further design a contrastive learning loss for hard samples. Thus, the model could automatically increase the weight of hard samples while reducing the weight of easy samples. The superior of MVC-HSM is verified by extensive experiments on public multi-view datasets, demonstrating the proposed MVC-HSM outperforms other state-of-the-art multi-view clustering.
期刊介绍:
Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.