Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/129521
題名: | Tensor Decomposition for Dimension Reduction | 作者: | 黃子銘 Huang, Su-Yun Huang, Tzee-Ming Cheng, Yu-Hsiang |
貢獻者: | 統計系 | 日期: | Jul-2019 | 上傳時間: | 28-Apr-2020 | 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. | 關聯: | Wiley Interdisciplinary Reviews: Computational Statistics,12:2 | 資料類型: | article | DOI: | https://doi.org/10.1002/wics.1482 |
Appears in Collections: | 期刊論文 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
index.html | 121 B | HTML2 | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.