Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning | |
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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 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/78706 ) |