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Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes 期刊论文
IEEE TRANSACTIONS ON SERVICES COMPUTING, 2022, 卷号: 15, 期号: 3, 页码: 1334-1344
作者:  Wu, Hao;  Luo, Xin;  Zhou, Mengchu
收藏  |  浏览/下载：63/0  |  提交时间：2022/08/22
High-dimensional and sparse tensor  missing data  latent factor analysis  temporal pattern  non-negativity  non-negative latent factorization of tensor  regularization  ensemble  services computing  
An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 6, 页码: 3522-3532
作者:  Luo, Xin;  Wang, Zidong;  Shang, Mingsheng
收藏  |  浏览/下载：79/0  |  提交时间：2021/08/20
High-dimensional and sparse (HiDS) data  industrial application  instance-frequency  non-negative latent factor analysis (NLFA)  recommender system  regularization  
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems 期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2021, 卷号: 7, 期号: 1, 页码: 227-240
作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Wu, Di;  Liu, Zhigang;  Shang, Mingsheng
收藏  |  浏览/下载：142/0  |  提交时间：2021/05/17
Data models  Training  Sparse matrices  Recommender systems  Computational modeling  Big Data  Scalability  Non-negative latent factor analysis  non-negativity  latent factor analysis  unconstrained optimization  high-dimensional and sparse matrix  collaborative filtering  recommender system  big data  
Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 5, 页码: 1844-1855
作者:  Luo, Xin;  Zhou, MengChu;  Li, Shuai;  Hu, Lun;  Shang, Mingsheng
收藏  |  浏览/下载：121/0  |  提交时间：2020/08/24
Computational modeling  Data models  Sparse matrices  Linear programming  Training  Convergence  Analytical models  Alternating-direction-method of multipliers  high-dimensional and sparse matrix  industrial application  non-negative latent factor analysis  recommender system  
Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 5, 页码: 1798-1809
作者:  Luo, Xin;  Wu, Hao;  Yuan, Huaqiang;  Zhou, MengChu
收藏  |  浏览/下载：369/0  |  提交时间：2020/08/24
Quality of service  Hidden Markov models  Data models  Training  Web services  Time factors  Latent factor analysis (LFA)  latent factorization of tensor  learning temporal pattern  linear bias (LB)  non-negative latent factorization of tensor  non-negativity constraint  quality-of-service (QoS) prediction  
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications 会议论文
15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018, Zhuhai, China, March 27, 2018 - March 29, 2018
作者:  Chen, Jia;  Luo, Xin
Adobe PDF(5259Kb)  |  收藏  |  浏览/下载：129/0  |  提交时间：2019/06/25
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：413/0  |  提交时间：2019/06/26
Unconstrained Non-negative Factorization of High-dimensional and Sparse Matrices in Recommender Systems 会议论文
14th IEEE International Conference on Automation Science and Engineering, CASE 2018, Munich, Germany, August 20, 2018 - August 24, 2018
作者:  Luo, Xin;  Zhou, Mengchu
收藏  |  浏览/下载：80/0  |  提交时间：2019/06/25
Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 卷号: 13, 期号: 6, 页码: 3098-3107
作者:  Luo, Xin;  Sun, Jianpei;  Wang, Zidong;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(803Kb)  |  收藏  |  浏览/下载：425/0  |  提交时间：2018/03/05
Big data application  high-dimensional, and sparse (SHiDS) matrix  nonnegative latent factor (NLF) model  symmetry  undirected HiDS network  
Efficient extraction of non-negative latent factors from high-dimensional and sparse matrices in industrial applications 会议论文
16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Catalonia, Spain, December 12, 2016 - December 15, 2016
作者:  Luo, Xin;  Shang, Mingsheng;  Li, Shuai
收藏  |  浏览/下载：54/0  |  提交时间：2018/03/16