| dc.contributor | 統計系 | |
| dc.creator (作者) | 吳漢銘 | |
| dc.creator (作者) | Wu, Han-Ming | |
| dc.creator (作者) | Yao, Wei-Ting | |
| dc.date (日期) | 2013-09 | |
| dc.date.accessioned | 2022-04-12 | - |
| dc.date.available | 2022-04-12 | - |
| dc.date.issued (上傳時間) | 2022-04-12 | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/139844 | - |
| dc.description.abstract (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. | |
| dc.format.extent | 1273598 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Statistics & Computing, Vol.23, No. 5, pp.563-576 | |
| dc.subject (關鍵詞) | K-means clustering;Isometric feature mapping (ISOMAP);Nonlinear dimension reduction;Nonlinear manifold;Rank-two ellipse seriation;Sliced inverse regression | |
| dc.title (題名) | Isometric sliced inverse regression for nonlinear manifolds learning | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1007/s11222-012-9330-z | |
| dc.doi.uri (DOI) | https://doi.org/10.1007/s11222-012-9330-z | |
