{"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}
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.
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