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中国科学院重庆绿色智能技术研究院机构知识库
KMS Chongqing Institute of Green and Intelligent Technology, CAS
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罗辛 [15]
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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:64/0
  |  
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
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  |  
浏览/下载:82/0
  |  
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
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浏览/下载:148/0
  |  
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
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浏览/下载:124/0
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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
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浏览/下载:374/0
  |  
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)
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浏览/下载:131/0
  |  
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:416/0
  |  
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
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  |  
浏览/下载:84/0
  |  
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)
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  |  
浏览/下载:428/0
  |  
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
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  |  
浏览/下载:56/0
  |  
提交时间:2018/03/16