{"title":"高维数据的最佳投影","authors":"E. Corchado, C. Fyfe","doi":"10.1109/ICDM.2002.1184006","DOIUrl":null,"url":null,"abstract":"In this paper, we compare two artificial neural network algorithms for performing Exploratory Projection Pursuit, a statistical technique for investigating data by projecting it onto lower dimensional manifolds. The neural networks are extensions of a network which performs Principal Component Analysis. We illustrate the technique on artificial data before applying it to real data.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Optimal projections of high dimensional data\",\"authors\":\"E. Corchado, C. Fyfe\",\"doi\":\"10.1109/ICDM.2002.1184006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we compare two artificial neural network algorithms for performing Exploratory Projection Pursuit, a statistical technique for investigating data by projecting it onto lower dimensional manifolds. The neural networks are extensions of a network which performs Principal Component Analysis. We illustrate the technique on artificial data before applying it to real data.\",\"PeriodicalId\":405340,\"journal\":{\"name\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2002 IEEE International Conference on Data Mining, 2002. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDM.2002.1184006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDM.2002.1184006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we compare two artificial neural network algorithms for performing Exploratory Projection Pursuit, a statistical technique for investigating data by projecting it onto lower dimensional manifolds. The neural networks are extensions of a network which performs Principal Component Analysis. We illustrate the technique on artificial data before applying it to real data.