{"title":"利用高阶陌生人增强多视角聚类的类间可分性","authors":"Chundan Liu;Qian Zhang;Yongyong Chen;Junyu Dong;Chong Peng","doi":"10.1109/LSP.2024.3455988","DOIUrl":null,"url":null,"abstract":"Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing Inter-Class Separability With High-Order Strangers for Multi-View Clustering\",\"authors\":\"Chundan Liu;Qian Zhang;Yongyong Chen;Junyu Dong;Chong Peng\",\"doi\":\"10.1109/LSP.2024.3455988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10669096/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10669096/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Enhancing Inter-Class Separability With High-Order Strangers for Multi-View Clustering
Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.