加权网格主成分分析哈希

Xiancheng Zhou, Zhi-Qian Huang, Wing W. Y. Ng
{"title":"加权网格主成分分析哈希","authors":"Xiancheng Zhou, Zhi-Qian Huang, Wing W. Y. Ng","doi":"10.1109/ICMLC.2014.7009117","DOIUrl":null,"url":null,"abstract":"Principal Component Analysis (PCA) is one of the most widely used components of hashing. In this paper, we propose three PCA-based hashing methods to improve the performance of the Principal Component Hashing (PCH). Different principal components have different among of variances of data. In the PCH, each principal component corresponds to a hash function. Hence, the PCH treats each principal component to have the same importance which will lead to the loss of much information in constructing hashing table. To deal with this shortage, we propose the weighted PCH (WPCH), the grid PCH (GPCH) and the weighted grid PCH (WGPCH).","PeriodicalId":335296,"journal":{"name":"2014 International Conference on Machine Learning and Cybernetics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Weighted grid Principal Component Analysis hashing\",\"authors\":\"Xiancheng Zhou, Zhi-Qian Huang, Wing W. Y. Ng\",\"doi\":\"10.1109/ICMLC.2014.7009117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Principal Component Analysis (PCA) is one of the most widely used components of hashing. In this paper, we propose three PCA-based hashing methods to improve the performance of the Principal Component Hashing (PCH). Different principal components have different among of variances of data. In the PCH, each principal component corresponds to a hash function. Hence, the PCH treats each principal component to have the same importance which will lead to the loss of much information in constructing hashing table. To deal with this shortage, we propose the weighted PCH (WPCH), the grid PCH (GPCH) and the weighted grid PCH (WGPCH).\",\"PeriodicalId\":335296,\"journal\":{\"name\":\"2014 International Conference on Machine Learning and Cybernetics\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2014.7009117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2014.7009117","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

主成分分析(PCA)是哈希中使用最广泛的成分之一。本文提出了三种基于pca的哈希算法来提高主成分哈希算法(PCH)的性能。不同的主成分对数据的方差有不同的影响。在PCH中,每个主成分对应一个哈希函数。因此,PCH认为每个主成分具有相同的重要性,这将导致在构造哈希表时丢失大量信息。为了解决这一不足,我们提出了加权PCH (WPCH)、网格PCH (GPCH)和加权网格PCH (WGPCH)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted grid Principal Component Analysis hashing
Principal Component Analysis (PCA) is one of the most widely used components of hashing. In this paper, we propose three PCA-based hashing methods to improve the performance of the Principal Component Hashing (PCH). Different principal components have different among of variances of data. In the PCH, each principal component corresponds to a hash function. Hence, the PCH treats each principal component to have the same importance which will lead to the loss of much information in constructing hashing table. To deal with this shortage, we propose the weighted PCH (WPCH), the grid PCH (GPCH) and the weighted grid PCH (WGPCH).
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