期刊論文
| 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 ) |