解决增量半监督支持向量学习的平衡约束问题

Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, J. Pei, Heng Huang
{"title":"解决增量半监督支持向量学习的平衡约束问题","authors":"Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, J. Pei, Heng Huang","doi":"10.1145/3292500.3330962","DOIUrl":null,"url":null,"abstract":"Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with S3VM (denoted as BCS3VM). Recently, a novel incremental learning algorithm (IL-S3VM) based on the path following technique was proposed to significantly scale up S3VM. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling BCS3VM. To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into S3VM. More importantly, we provide the finite convergence analysis for our IL-BCS3VM algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-BCS3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.","PeriodicalId":186134,"journal":{"name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","volume":"26 13","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning\",\"authors\":\"Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, J. Pei, Heng Huang\",\"doi\":\"10.1145/3292500.3330962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with S3VM (denoted as BCS3VM). Recently, a novel incremental learning algorithm (IL-S3VM) based on the path following technique was proposed to significantly scale up S3VM. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling BCS3VM. To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into S3VM. More importantly, we provide the finite convergence analysis for our IL-BCS3VM algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-BCS3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.\",\"PeriodicalId\":186134,\"journal\":{\"name\":\"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\",\"volume\":\"26 13\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3292500.3330962\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3292500.3330962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

半监督支持向量机(S3VM)是目前最流行的半监督学习方法之一。为了避免将所有未标记的示例分类到同一类的琐碎解决方案,平衡约束通常与S3VM一起使用(表示为BCS3VM)。最近,一种新的基于路径跟踪技术的增量学习算法(IL-S3VM)被提出,以显着扩展S3VM。然而,平衡约束与先前标记和未标记样本的动态关系阻碍了他们处理BCS3VM的增量方法。为了填补这一空白,本文提出了一种新的基于IL-S3VM的增量式S3VM算法(IL-BCS3VM),该算法可以有效地处理平衡约束并直接更新BCS3VM的解。具体来说,为了处理平衡约束与之前标记和未标记样本之间的动态关系,我们设计了两个独特的程序,分别可以消除和添加平衡约束到S3VM中。更重要的是,我们对我们的IL-BCS3VM算法进行了有限收敛分析。在各种基准数据集上的实验结果不仅证实了IL-BCS3VM的有限收敛性,而且与现有的批处理和增量学习算法相比,IL-BCS3VM的计算时间大大减少,同时保持了相似的泛化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning
Semi-Supervised Support Vector Machine (S3VM) is one of the most popular methods for semi-supervised learning. To avoid the trivial solution of classifying all the unlabeled examples to a same class, balancing constraint is often used with S3VM (denoted as BCS3VM). Recently, a novel incremental learning algorithm (IL-S3VM) based on the path following technique was proposed to significantly scale up S3VM. However, the dynamic relationship of balancing constraint with previous labeled and unlabeled samples impede their incremental method for handling BCS3VM. To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. Specifically, to handle the dynamic relationship of balancing constraint with previous labeled and unlabeled samples, we design two unique procedures which can respectively eliminate and add the balancing constraint into S3VM. More importantly, we provide the finite convergence analysis for our IL-BCS3VM algorithm. Experimental results on a variety of benchmark datasets not only confirm the finite convergence of IL-BCS3VM, but also show a huge reduction of computational time compared with existing batch and incremental learning algorithms, while retaining the similar generalization performance.
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