| dc.contributor | 統計系 | |
| dc.creator (作者) | 黃子銘 | |
| dc.creator (作者) | Huang, Su-Yun | |
| dc.creator (作者) | Huang, Tzee-Ming | |
| dc.creator (作者) | Cheng, Yu-Hsiang | |
| dc.date (日期) | 2019-07 | |
| dc.date.accessioned | 28-Apr-2020 13:46:38 (UTC+8) | - |
| dc.date.available | 28-Apr-2020 13:46:38 (UTC+8) | - |
| dc.date.issued (上傳時間) | 28-Apr-2020 13:46:38 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/129521 | - |
| dc.description.abstract (摘要) | Tensor data are data with multiway array structure. They are often very high dimensional and are routinely encountered in many scientific fields. Dimension reduction is the technique of reducing the number of underlying variables for compressed data representation and for model parsimony. Tensor dimension reduction aims for reducing the tensor data dimension while keeping data`s tensor structure. | |
| dc.format.extent | 121 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Wiley Interdisciplinary Reviews: Computational Statistics,12:2 | |
| dc.title (題名) | Tensor Decomposition for Dimension Reduction | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1002/wics.1482 | |
| dc.doi.uri (DOI) | https://doi.org/10.1002/wics.1482 | |
