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：Pioneer Hundred Talents Program of Chinese Academy of Sciences

已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序发表日期升序发表日期降序题名升序题名降序WOS被引频次升序WOS被引频次降序提交时间升序提交时间降序期刊影响因子升序期刊影响因子降序
	Hierarchical Particle Swarm Optimization-incorporated Latent Factor Analysis for Large-Scale Incomplete Matrices 期刊论文 IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 6, 页码: 1524-1536 作者:  Chen, Jia;  Luo, Xin;  Zhou, Mengchu 收藏  |  浏览/下载：64/0  |  提交时间：2022/12/26 Adaptation models  Optimization  Convergence  Computational modeling  Sparse matrices  Particle swarm optimization  Big Data  Big data  latent factor analysis  particle swarm optimization  high-dimensional and sparse matrix  large-scale incomplete data  missing data estimation  industrial application
	Non-Negative Latent Factor Model Based on beta-Divergence for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 8, 页码: 4612-4623 作者:  Xin, Luo;  Yuan, Ye;  Zhou, MengChu;  Liu, Zhigang;  Shang, Mingsheng 收藏  |  浏览/下载：130/0  |  提交时间：2021/08/20 beta-divergence  big data  high-dimensional and sparse (HiDS) matrix  industrial application  learning algorithm  non-negative latent factor (NLF) analysis  recommender system
	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：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
	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：125/0  |  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
	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：217/0  |  提交时间：2018/06/04 Differential evolution (DE)  general framework  industrial application  positioning optimization  self-labeled  semi-supervised classification (SSC)