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 en_US
ISSN 0960-3174 1573-1375
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 紙本 電子版
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