中国科学院重庆绿色智能技术研究院机构知识库

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限定条件	资助项目：National Natural Science Foundation of China[62002337]

已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择期刊影响因子升序期刊影响因子降序作者升序作者降序题名升序题名降序WOS被引频次升序WOS被引频次降序发表日期升序发表日期降序提交时间升序提交时间降序
	A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis 期刊论文 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 页码: 15 作者:  Li, Jinli;  Luo, Xin;  Yuan, Ye;  Gao, Shangce 收藏  |  浏览/下载：182/0  |  提交时间：2023/12/25 High-dimensional and incomplete data  latent factor analysis  stochastic gradient descent  nonlinear proportional integral derivation  particle swarm optimization  parameter adaptation
	A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction 期刊论文 IEEE TRANSACTIONS ON SERVICES COMPUTING, 2023, 卷号: 16, 期号: 2, 页码: 802-814 作者:  Wu, Di;  Zhang, Peng;  He, Yi;  Luo, Xin 收藏  |  浏览/下载：11/0  |  提交时间：2023/12/25 Web service  service selection  Quality-of-Service (QoS)  latent factor analysis  missing data prediction  big data
	A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2022, 页码: 14 作者:  Yuan, Ye;  Luo, Xin;  Shang, Mingsheng;  Wang, Zidong 收藏  |  浏览/下载：65/0  |  提交时间：2022/08/22 Quality of service  Data models  Kalman filters  Estimation  Computational modeling  Web services  Heuristic algorithms  Alternating least squares (ALSs)  computational intelligence  data science  dynamic latent factor analysis (LFA)  dynamics  intelligent computing  Kalman filter  temporal pattern  Web service
	A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 页码: 15 作者:  Wu, Di;  He, Yi;  Luo, Xin;  Zhou, MengChu 收藏  |  浏览/下载：44/0  |  提交时间：2022/08/22 Big data  computational intelligence  latent factor analysis (LFA)  missing data  online algorithm  online feature selection  sparse streaming feature  streaming feature
	An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14 作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Wang, Zidong 收藏  |  浏览/下载：42/0  |  提交时间：2022/08/22 High-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  L-1 norm  L-2 norm  recommender system (RS)
	Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data 期刊论文 IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2021, 卷号: 8, 期号: 4, 页码: 796-805 作者:  Wu, Di;  Luo, Xin 收藏  |  浏览/下载：82/0  |  提交时间：2021/05/17 High-dimensional and sparse matrix  L-1-norm  L-2-norm  latent factor model  recommender system  smooth L-1-norm
	An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13 作者:  Shang, Mingsheng;  Yuan, Ye;  Luo, Xin;  Zhou, MengChu 收藏  |  浏览/下载：55/0  |  提交时间：2022/08/22 Computational modeling  Sparse matrices  Convergence  Data models  Predictive models  Linear programming  Euclidean distance  -divergence  big data  convergence analysis  high-dimensional and sparse (HiDS) data  momentum  machine learning  missing data estimation  non-negative latent factor analysis (NLFA)  recommender system (RS)