教師資料查詢 | 類別: 期刊論文 | 教師: 吳漢銘 Han-ming Wu (瀏覽個人網頁)

標題:Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning
學年102
學期1
出版(發表)日期2013/09/01
作品名稱Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning
作品名稱(其他語言)
著者Yao, Wei-ting; Wu, Han-ming
單位淡江大學數學學系
出版者New York: Springer New York LLC
著錄名稱、卷期、頁數Statistics and Computing 23(5), pp.563-576
摘要Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data. In this study, we present isometric SIR for nonlinear dimension reduction, which is a hybrid of the SIR method using the geodesic distance approximation. First, the proposed method computes the isometric distance between data points; the resulting distance matrix is then sliced according to K-means clustering results, and the classical SIR algorithm is applied. We show that the isometric SIR (ISOSIR) can reveal the geometric structure of a nonlinear manifold dataset (e.g., the Swiss roll). We report and discuss this novel method in comparison to several existing dimension-reduction techniques for data visualization and classification problems. The results show that ISOSIR is a promising nonlinear feature extractor for classification applications.
關鍵字Hierarchical clustering; Isometric feature mapping (ISOMAP); Nonlinear dimension reduction; Nonlinear manifold; Rank-two ellipse seriation; Sliced inverse regression
語言英文(美國)
ISSN0960-3174;1573-1375
期刊性質國外
收錄於SCI
產學合作
通訊作者
審稿制度
國別美國
公開徵稿
出版型式紙本;電子版
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