Tensor Decomposition for Dimension Reduction

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

DC Field	Value	Language
dc.contributor	統計系
dc.creator	黃子銘
dc.creator	Huang, Su-Yun
dc.creator	Huang, Tzee-Ming
dc.creator	Cheng, Yu-Hsiang
dc.date	2019-07
dc.date.accessioned	2020-04-28T05:46:38Z	-
dc.date.available	2020-04-28T05:46:38Z	-
dc.date.issued	2020-04-28T05:46:38Z	-
dc.identifier.uri	http://nccur.lib.nccu.edu.tw/handle/140.119/129521	-
dc.description.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.
dc.format.extent	121 bytes	-
dc.format.mimetype	text/html	-
dc.relation	Wiley Interdisciplinary Reviews: Computational Statistics,12:2
dc.title	Tensor Decomposition for Dimension Reduction
dc.type	article
dc.identifier.doi	10.1002/wics.1482
dc.doi.uri	https://doi.org/10.1002/wics.1482
item.fulltext	With Fulltext	-
item.grantfulltext	restricted	-
item.cerifentitytype	Publications	-
item.openairetype	article	-
item.openairecristype	http://purl.org/coar/resource_type/c_18cf	-
Appears in Collections:	期刊論文

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