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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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Large-Scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems
期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 2, 页码: 420-431
作者:
Shi, Xiaoyu
;
He, Qiang
;
Luo, Xin
;
Bai, Yanan
;
Shang, Mingsheng
收藏
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浏览/下载:72/0
  |  
提交时间:2022/08/22
Recommender systems
Training
Optimization
Big Data
Cloud computing
Computational modeling
Sparse matrices
Recommender system
latent factor analysis
high-dimensional and sparse matrices
alternative stochastic gradient descent
distributed computing
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
Large-Scale Affine Matrix Rank Minimization With a Novel Nonconvex Regularizer
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 15
作者:
Wang, Zhi
;
Liu, Yu
;
Luo, Xin
;
Wang, Jianjun
;
Gao, Chao
;
Peng, Dezhong
;
Chen, Wu
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浏览/下载:52/0
  |  
提交时间:2022/08/22
Minimization
Convergence
Tensors
Optimization
Analytical models
Data models
Data analysis
Inexact proximal step
low-rank minimization
matrix completion
novel nonconvex regularizer
robust principal component analysis (RPCA)
tensor completion
Elastic-net regularized latent factor analysis-based models for recommender systems
期刊论文
NEUROCOMPUTING, 2019, 卷号: 329, 页码: 66-74
作者:
Wang, Dexian
;
Chen, Yanbin
;
Guo, Junxiao
;
Shi, Xiaoyu
;
He, Chunlin
;
Luo, Xin
;
Yuan, Huaqiang
Adobe PDF(1965Kb)
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浏览/下载:213/0
  |  
提交时间:2019/01/17
Big data
Recommender systems
Collaborative filtering
Latent factor analysis
Elastic-net
Regularization
Latent factor distribution
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:
Li, Shuai
;
Zhou, MengChu
;
Luo, Xin
Adobe PDF(2696Kb)
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浏览/下载:294/0
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提交时间:2018/11/01
Dual neural network
kinematic control
redundancy resolution
robotic manipulator
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:
Li, Shuai
;
Zhou, MengChu
;
Luo, Xin
Adobe PDF(2696Kb)
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浏览/下载:289/0
  |  
提交时间:2019/06/26
Dual neural network
kinematic control
redundancy resolution
robotic manipulator
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
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)
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浏览/下载:416/0
  |  
提交时间:2019/06/26
Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model
期刊论文
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 卷号: 484, 页码: 506-515
作者:
Abbas, Khushnood
;
Shang, Mingsheng
;
Luo, Xin
;
Abbasi, Alireza
Adobe PDF(2072Kb)
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浏览/下载:102/0
  |  
提交时间:2018/03/05
Novelty
Evolving networks
Recommender systems
E-commerce
Collective behaviour
Trend prediction
Emerging behaviour
Highly Efficient Framework for Predicting Interactions Between Proteins
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 3, 页码: 731-743
作者:
You, Zhu-Hong
;
Zhou, MengChu
;
Luo, Xin
;
Li, Shuai
Adobe PDF(1407Kb)
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浏览/下载:221/0
  |  
提交时间:2018/03/15
Big data
feature extraction
kernel extreme learning machine (K-ELM)
low-rank approximation (LRA)
protein-protein interactions (PPIs)
support vector machine (SVM)