非定量数据和大数据环境下PLS回归的研究现状

Proceedings of the 4th International Conference on Networking, Information Systems & Security Pub Date : 2021-04-01 DOI:10.1145/3454127.3456615

Yasmina Al Marouni, Youssef Bentaleb

{"title":"非定量数据和大数据环境下PLS回归的研究现状","authors":"Yasmina Al Marouni, Youssef Bentaleb","doi":"10.1145/3454127.3456615","DOIUrl":null,"url":null,"abstract":"Partial Least Squares Regression (PLSR) is a data analysis method in high-dimensional settings, it is used as a dimension reduction method and also as a tool of linear regression. However, it has some problems in a big data context when the data is too large and has been designed to handle only quantitative variables.In this paper, we will present PLSR, then discuss adaptations and extensions of PLS regression to overcome these problems so that it can be more use-full in a big data context.","PeriodicalId":432206,"journal":{"name":"Proceedings of the 4th International Conference on Networking, Information Systems & Security","volume":"169 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"State of art of PLS Regression for non quantitative data and in Big Data context\",\"authors\":\"Yasmina Al Marouni, Youssef Bentaleb\",\"doi\":\"10.1145/3454127.3456615\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Partial Least Squares Regression (PLSR) is a data analysis method in high-dimensional settings, it is used as a dimension reduction method and also as a tool of linear regression. However, it has some problems in a big data context when the data is too large and has been designed to handle only quantitative variables.In this paper, we will present PLSR, then discuss adaptations and extensions of PLS regression to overcome these problems so that it can be more use-full in a big data context.\",\"PeriodicalId\":432206,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Networking, Information Systems & Security\",\"volume\":\"169 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Networking, Information Systems & Security\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3454127.3456615\",\"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 4th International Conference on Networking, Information Systems & Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3454127.3456615","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

偏最小二乘回归(PLSR)是一种高维环境下的数据分析方法，它是一种降维方法，也是线性回归的一种工具。然而，在大数据背景下，当数据太大并且被设计为只能处理定量变量时，它就会出现一些问题。在本文中，我们将介绍PLS回归，然后讨论PLS回归的适应和扩展，以克服这些问题，使其在大数据环境中更有用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

State of art of PLS Regression for non quantitative data and in Big Data context

Partial Least Squares Regression (PLSR) is a data analysis method in high-dimensional settings, it is used as a dimension reduction method and also as a tool of linear regression. However, it has some problems in a big data context when the data is too large and has been designed to handle only quantitative variables.In this paper, we will present PLSR, then discuss adaptations and extensions of PLS regression to overcome these problems so that it can be more use-full in a big data context.

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

Proceedings of the 4th International Conference on Networking, Information Systems & Security

自引率

0.00%

发文量