标签不完全多视图多标签分类的标签恢复与标签关联协同学习。

Zhi-Fen He, Chun-Hua Zhang, Bin Liu, Bo Li
{"title":"标签不完全多视图多标签分类的标签恢复与标签关联协同学习。","authors":"Zhi-Fen He,&nbsp;Chun-Hua Zhang,&nbsp;Bin Liu,&nbsp;Bo Li","doi":"10.1007/s10489-022-03945-y","DOIUrl":null,"url":null,"abstract":"<p><p>Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, <i>label recovery and label correlation co-learning for</i> <b>M</b> <i>ulti</i>-<b>V</b> <i>iew</i> <b>M</b> <i>ulti</i>-<b>L</b> <i>abel classification with inco</i> <b>M</b> <i>plete</i> <b>L</b> <i>abels</i> (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.</p>","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"53 8","pages":"9444-9462"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360669/pdf/","citationCount":"2","resultStr":"{\"title\":\"Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.\",\"authors\":\"Zhi-Fen He,&nbsp;Chun-Hua Zhang,&nbsp;Bin Liu,&nbsp;Bo Li\",\"doi\":\"10.1007/s10489-022-03945-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, <i>label recovery and label correlation co-learning for</i> <b>M</b> <i>ulti</i>-<b>V</b> <i>iew</i> <b>M</b> <i>ulti</i>-<b>L</b> <i>abel classification with inco</i> <b>M</b> <i>plete</i> <b>L</b> <i>abels</i> (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.</p>\",\"PeriodicalId\":72260,\"journal\":{\"name\":\"Applied intelligence (Dordrecht, Netherlands)\",\"volume\":\"53 8\",\"pages\":\"9444-9462\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9360669/pdf/\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied intelligence (Dordrecht, Netherlands)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s10489-022-03945-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied intelligence (Dordrecht, Netherlands)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10489-022-03945-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

多视图多标签学习(MVML)是机器学习中的一个重要范例,其中每个实例由几个异构视图表示,并与一组类标签相关联。然而,标签不完整以及忽略视图之间的关系和标签之间的相关性将导致MVML算法的性能下降。基于此,本文提出了一种新的方法——标签恢复和标签相关共同学习,用于包含M个完整L标签的M个多v视图M个多L标签分类。首先,为每个标签构建一个基于标签相关引导的二值分类器。然后,采用多核融合方法,利用多视图之间的个体信息和互补信息,区分各视图的贡献差异,对多视图数据进行有效融合;最后,我们提出了一种协同学习策略,该策略同时考虑了非对称标签相关性的利用、多视图数据的融合、不完整标签矩阵的恢复和分类模型的构建。这样,不完全标签矩阵的恢复和标签相关性的学习相互作用,相互促进,指导分类器的训练。大量的实验结果表明,根据六个评估标准,MV2ML在各种现实世界的多视图多标签数据集上取得了与最先进的方法相比极具竞争力的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels.

Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, label recovery and label correlation co-learning for M ulti-V iew M ulti-L abel classification with inco M plete L abels (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信