{"title":"基于流形学习和蚁群算法的Web文档检索","authors":"Ziqiang Wang, Sun Xia","doi":"10.1109/ICBNMT.2009.5348468","DOIUrl":null,"url":null,"abstract":"To efficiently deal with high dimensionality and precision problems in document retrieval, a novel document retrieval algorithm based on manifold learning and ant colony optimization(ACO) algorithm is proposed. The high-dimensional document data are first projected into lower-dimensional feature space with neighborhood preserving embedding (NPE) algorithm, the ACO algorithm is then applied to retrieve relevant documents in the reduced lower-dimensionality document feature space. Extensive experiments on real-world data set demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":267128,"journal":{"name":"2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Web document retrieval using manifold learning and ACO algorithm\",\"authors\":\"Ziqiang Wang, Sun Xia\",\"doi\":\"10.1109/ICBNMT.2009.5348468\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To efficiently deal with high dimensionality and precision problems in document retrieval, a novel document retrieval algorithm based on manifold learning and ant colony optimization(ACO) algorithm is proposed. The high-dimensional document data are first projected into lower-dimensional feature space with neighborhood preserving embedding (NPE) algorithm, the ACO algorithm is then applied to retrieve relevant documents in the reduced lower-dimensionality document feature space. Extensive experiments on real-world data set demonstrate the effectiveness of the proposed algorithm.\",\"PeriodicalId\":267128,\"journal\":{\"name\":\"2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBNMT.2009.5348468\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBNMT.2009.5348468","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Web document retrieval using manifold learning and ACO algorithm
To efficiently deal with high dimensionality and precision problems in document retrieval, a novel document retrieval algorithm based on manifold learning and ant colony optimization(ACO) algorithm is proposed. The high-dimensional document data are first projected into lower-dimensional feature space with neighborhood preserving embedding (NPE) algorithm, the ACO algorithm is then applied to retrieve relevant documents in the reduced lower-dimensionality document feature space. Extensive experiments on real-world data set demonstrate the effectiveness of the proposed algorithm.