{"title":"室内定位的核Fisher判别分析","authors":"Nhan Vo Than Ngo, Kyung Yong Park, J. G. Kim","doi":"10.7236/IJASC.2015.4.2.177","DOIUrl":null,"url":null,"abstract":"In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.","PeriodicalId":297506,"journal":{"name":"The International Journal of Advanced Smart Convergence","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Kernel Fisher Discriminant Analysis for Indoor Localization\",\"authors\":\"Nhan Vo Than Ngo, Kyung Yong Park, J. G. Kim\",\"doi\":\"10.7236/IJASC.2015.4.2.177\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.\",\"PeriodicalId\":297506,\"journal\":{\"name\":\"The International Journal of Advanced Smart Convergence\",\"volume\":\"124 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The International Journal of Advanced Smart Convergence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7236/IJASC.2015.4.2.177\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Journal of Advanced Smart Convergence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7236/IJASC.2015.4.2.177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Kernel Fisher Discriminant Analysis for Indoor Localization
In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.
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