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Graph Regularized Lp Smooth Non-negative Matrix Factorization for Data Representation

Chengcai Leng Hai Zhang Guorong Cai Irene Cheng Anup Basu

自动化学报（英文版）2019，Vol.6Issue(2)：584-595,12.

自动化学报（英文版）2019，Vol.6Issue(2)：584-595,12.DOI:10.1109/JAS.2019.1911417

Graph Regularized Lp Smooth Non-negative Matrix Factorization for Data Representation

Chengcai Leng ¹Hai Zhang ²Guorong Cai ³Irene Cheng ¹Anup Basu⁴

作者信息

1. School of Mathematics, Northwest University, Xi'an 710127
2. School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang 330063, China
3. Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
4. Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China
折叠

摘要

关键词

Data clustering/dimensionality reduction/graph regularization/Lp smooth non-negative matrix factorization (SNMF).

Key words

Data clustering/dimensionality reduction/graph regularization/Lp smooth non-negative matrix factorization (SNMF).

引用本文复制引用

Chengcai Leng,Hai Zhang,Guorong Cai,Irene Cheng,Anup Basu..Graph Regularized Lp Smooth Non-negative Matrix Factorization for Data Representation[J].自动化学报（英文版）,2019,6(2):584-595,12.

基金项目

This work was supported by the National Natural Science Foundation of China (61702251,61363049,11571011),the State Scholarship Fund of China Scholarship Council (CSC) (201708360040),the Natural Science Foundation of Jiangxi Province (20161BAB212033),the Natural Science Basic Research Plan in Shaanxi Province of China (2018JM6030),the Doctor Scientific Research Starting Foundation of Northwest University (338050050) and Youth Academic Talent Support Program of Northwest University. （61702251,61363049,11571011）

自动化学报（英文版）

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