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Multiview partial least squares

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成果类型:
期刊论文
作者:
Mou, Yi*;Zhou, Long;You, Xinge;Lu, Yaling;Chen, Weizhen;...
通讯作者:
Mou, Yi
作者机构:
[Zhou, Long; Lu, Yaling; Zhao, Xu; Mou, Yi; Chen, Weizhen] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China.
[You, Xinge] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Peoples R China.
通讯机构:
[Mou, Yi] W
Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China.
语种:
英文
关键词:
Multiview data;Partial least squares;Regression
期刊:
Chemometrics and Intelligent Laboratory Systems
ISSN:
0169-7439
年:
2017
卷:
160
页码:
13-21
基金类别:
This work is supported by Hubei Provincial Department of Education (No. D20161705 ) Science & Technology Department of Hubei Province(No.2016CFB298), Grain Administration of Hubei Province and Research and Innovation Initiatives of WHPU (No. 2016J06 ).
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
摘要:
In practice, multiple distinct features are need to comprehensively analyze complex samples. In machine learning, data set obtained with a feature extractor is referred as a view. Most of data used in practics are collected with various feature extractors. It is practical to assume that an individual view is unlikely to be sufficient for effective analyzing the property of the sample. Therefore, integration of multiview information is both valuable and necessary. But, traditional partial least squares is proposed for single view high dimensiona...

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