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An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, MengChu;  Li, Shuai;  Shang, MingSheng
收藏  |  浏览/下载:508/0  |  提交时间:2018/07/02
Big data  high-dimensional and sparse matrix  learning algorithms  missing-data estimation  nonnegative latent factor analysis  optimization methods recommender system  
A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 3, 页码: 909-920
作者:  Wu, Di;  Luo, Xin;  Wang, Guoyin;  Shang, Mingsheng;  Yuan, Ye;  Yan, Huyong
收藏  |  浏览/下载:213/0  |  提交时间:2018/06/04
Differential evolution (DE)  general framework  industrial application  positioning optimization  self-labeled  semi-supervised classification (SSC)  
On minimizing total energy consumption in the scheduling of virtual machine reservations 期刊论文
Journal of Network and Computer Applications, 2018, 卷号: 113, 页码: 64-74
作者:  Tian, Wenhong;  He, Majun;  Guo, Wenxia;  Huang, Wenqiang;  Shi, Xiaoyu;  Shang, Mingsheng;  Toosi, Adel Nadjaran;  Buyya, Rajkumar
Adobe PDF(4775Kb)  |  收藏  |  浏览/下载:139/0  |  提交时间:2019/06/26
An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications 期刊论文
IEEE Transactions on Industrial Informatics, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(805Kb)  |  收藏  |  浏览/下载:416/0  |  提交时间:2019/06/26