{"title":"Nonlinear component analysis by fuzzy clustering and multidimensional scaling methods","authors":"Eriko Ikeda, T. Imaoka, H. Ichihashi, T. Miyoshi","doi":"10.1109/IJCNN.1999.833473","DOIUrl":null,"url":null,"abstract":"This paper proposes a new strategy of nonlinear component analysis for dimensionality reduction and representation of multidimensional data sets. The proposed procedure consists of two steps: one is to partition the data set into several clusters based on the local distances between two points, and the other is to project the obtained sub-manifolds on a low dimensional linear space by the multidimensional scaling methods.","PeriodicalId":157719,"journal":{"name":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1999.833473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper proposes a new strategy of nonlinear component analysis for dimensionality reduction and representation of multidimensional data sets. The proposed procedure consists of two steps: one is to partition the data set into several clusters based on the local distances between two points, and the other is to project the obtained sub-manifolds on a low dimensional linear space by the multidimensional scaling methods.
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