| dc.contributor | 統計系 | |
| dc.creator (作者) | 吳漢銘 | |
| dc.creator (作者) | Wu, Han-Ming | |
| dc.creator (作者) | Kao, Chiun-How | |
| dc.creator (作者) | Chen, Chun-houh | |
| dc.date (日期) | 2020-01 | |
| 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/140.119/139838 | - |
| dc.description.abstract (摘要) | Sliced inverse regression (SIR) is a popular slice-based sufficient dimension reduction technique for exploring the intrinsic structure of high-dimensional data. A main goal of dimension reduction is data visualization. This chapter reviews the extension of principal component analysis (PCA) to the interval-valued data, followed by a brief description of the classic SIR. It considers different families of symbolic-numerical-symbolic approaches to extend SIR to the interval-valued data. The chapter evaluates the implemented interval SIR methods and compare the results with those of interval PCA for low-dimensional discriminative and visualization purposes by means of simulation studies. The analysis of interval-valued data usually serves as the basic principle for analyzing other types of symbolic data, such as multi-valued data, modal-valued data, and modal multi-valued data. The advantage of the distributional approaches is that the resulting symbolic covariance matrix fully utilizes all the information in the data. | |
| dc.format.extent | 129 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Advances in Data Science: Symbolic, Complex and Network Data, John Wiley & Sons, Inc., pp.49-78 | |
| dc.subject (關鍵詞) | data visualization;dimension reduction;distributional approaches;interval-valued data;simulation studies;sliced inverse regression method;symbolic covariance matrix;symbolic-numerical-symbolic approaches | |
| dc.title (題名) | Dimension reduction and visualization of symbolic interval-valued data using sliced inverse regression | |
| dc.type (資料類型) | book/chapter | |
| dc.identifier.doi (DOI) | 10.1002/9781119695110.ch3 | |
| dc.doi.uri (DOI) | https://doi.org/10.1002/9781119695110.ch3 | |
