{"title":"撤回通知局部常规嵌入","authors":"Lu Tan, Yanrong Chi","doi":"10.1109/ICNC.2011.6022390","DOIUrl":null,"url":null,"abstract":"Introducing the topological structure and regular topology structure, the purpose is to seek with regular topological structure of low dimensional data set, the structural topological structure regularity, and puts forward the measure to keep data set topology structure of local rules embedding method. Compared to nuclear feature mapping methods, such as Locally Linear Embedding, Laplacian Eigenmap and so on, low dimensional embedded result is approximately regular, and data classification has more natural connection. The last results prove the theory results show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.","PeriodicalId":87274,"journal":{"name":"International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications","volume":"38 1","pages":"2133-2136"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Notice of Retraction Locally regular embedding\",\"authors\":\"Lu Tan, Yanrong Chi\",\"doi\":\"10.1109/ICNC.2011.6022390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introducing the topological structure and regular topology structure, the purpose is to seek with regular topological structure of low dimensional data set, the structural topological structure regularity, and puts forward the measure to keep data set topology structure of local rules embedding method. Compared to nuclear feature mapping methods, such as Locally Linear Embedding, Laplacian Eigenmap and so on, low dimensional embedded result is approximately regular, and data classification has more natural connection. The last results prove the theory results show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.\",\"PeriodicalId\":87274,\"journal\":{\"name\":\"International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications\",\"volume\":\"38 1\",\"pages\":\"2133-2136\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2011.6022390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2011.6022390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Introducing the topological structure and regular topology structure, the purpose is to seek with regular topological structure of low dimensional data set, the structural topological structure regularity, and puts forward the measure to keep data set topology structure of local rules embedding method. Compared to nuclear feature mapping methods, such as Locally Linear Embedding, Laplacian Eigenmap and so on, low dimensional embedded result is approximately regular, and data classification has more natural connection. The last results prove the theory results show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.