Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/129521


Title: Tensor Decomposition for Dimension Reduction
Authors: 黃子銘
Huang, Su-Yun
Huang, Tzee-Ming
Cheng, Yu-Hsiang
Contributors: 統計系
Date: 2019-07
Issue Date: 2020-04-28 13:46:38 (UTC+8)
Abstract: 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.
Relation: Wiley Interdisciplinary Reviews: Computational Statistics,12:2
Data Type: article
DOI 連結: https://doi.org/10.1002/wics.1482
Appears in Collections:[應用數學系] 期刊論文

Files in This Item:

File Description SizeFormat
index.html0KbHTML126View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing