基于核保持嵌入和随机游动的离群点检测

Enhui Li, Huawen Liu, Kaile Su, Shichao Zhang
{"title":"基于核保持嵌入和随机游动的离群点检测","authors":"Enhui Li, Huawen Liu, Kaile Su, Shichao Zhang","doi":"10.1109/ICBK50248.2020.00013","DOIUrl":null,"url":null,"abstract":"Since outlier detection has a wide range of potential applications, it has become a fundamental and hot research topic in data mining. Recently, the technique of self-representation has attracted extensive attention and many low-rank representation based outlier detection algorithms have been witnessed. However, most of them pay more attention to minimize the reconstruction error of the data, without involving the manifold structure. Meanwhile, as a low-rank constraint, the single nuclear norm often leads to suboptimal solution. To alleviate these problems, in this paper, we propose a novel outlier detection method, which adopts kernel-based distance to retain the overall relations. Moreover, the double nuclear norm is exploited to address the suboptimal problem. Further, a tailored random walk is used to identify outliers, after the similarity relations of the data available. The extensive simulation experiments on five public datasets demonstrate the superiority of the proposed method in comparing to the state-of-the-art outlier detection algorithms.","PeriodicalId":432857,"journal":{"name":"2020 IEEE International Conference on Knowledge Graph (ICKG)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Outlier Detection via Kernel Preserving Embedding and Random Walk\",\"authors\":\"Enhui Li, Huawen Liu, Kaile Su, Shichao Zhang\",\"doi\":\"10.1109/ICBK50248.2020.00013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since outlier detection has a wide range of potential applications, it has become a fundamental and hot research topic in data mining. Recently, the technique of self-representation has attracted extensive attention and many low-rank representation based outlier detection algorithms have been witnessed. However, most of them pay more attention to minimize the reconstruction error of the data, without involving the manifold structure. Meanwhile, as a low-rank constraint, the single nuclear norm often leads to suboptimal solution. To alleviate these problems, in this paper, we propose a novel outlier detection method, which adopts kernel-based distance to retain the overall relations. Moreover, the double nuclear norm is exploited to address the suboptimal problem. Further, a tailored random walk is used to identify outliers, after the similarity relations of the data available. The extensive simulation experiments on five public datasets demonstrate the superiority of the proposed method in comparing to the state-of-the-art outlier detection algorithms.\",\"PeriodicalId\":432857,\"journal\":{\"name\":\"2020 IEEE International Conference on Knowledge Graph (ICKG)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Knowledge Graph (ICKG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBK50248.2020.00013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Knowledge Graph (ICKG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBK50248.2020.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

由于离群点检测具有广泛的潜在应用,它已成为数据挖掘领域的一个基础和热点研究课题。近年来,自表示技术受到了广泛的关注,并出现了许多基于低秩表示的离群点检测算法。然而,这些方法大多侧重于最小化数据的重构误差,而不涉及流形结构。同时,作为低阶约束,单核范数往往导致次优解。为了解决这些问题,本文提出了一种新的异常点检测方法,该方法采用基于核的距离来保留整体关系。此外,利用双核规范来解决次优问题。此外,在可用数据的相似关系之后,使用定制随机漫步来识别异常值。在五个公共数据集上的大量模拟实验表明,与最先进的离群值检测算法相比,所提出的方法具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outlier Detection via Kernel Preserving Embedding and Random Walk
Since outlier detection has a wide range of potential applications, it has become a fundamental and hot research topic in data mining. Recently, the technique of self-representation has attracted extensive attention and many low-rank representation based outlier detection algorithms have been witnessed. However, most of them pay more attention to minimize the reconstruction error of the data, without involving the manifold structure. Meanwhile, as a low-rank constraint, the single nuclear norm often leads to suboptimal solution. To alleviate these problems, in this paper, we propose a novel outlier detection method, which adopts kernel-based distance to retain the overall relations. Moreover, the double nuclear norm is exploited to address the suboptimal problem. Further, a tailored random walk is used to identify outliers, after the similarity relations of the data available. The extensive simulation experiments on five public datasets demonstrate the superiority of the proposed method in comparing to the state-of-the-art outlier detection algorithms.
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:604180095
Book学术官方微信