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• 重庆绿色智能技术研... [26]
• 自动化研究所 [1]

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• OAI收割 [27]

内容类型

• 期刊论文 [22]
• 会议论文 [5]

发表日期

• 2022 [1]
• 2021 [13]
• 2020 [1]
• 2019 [5]
• 2018 [4]
• 2017 [3]
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浏览/检索结果: 共27条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices 期刊论文  OAI收割 IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 3, 页码: 784-794 作者:  Yuan, Ye;  He, Qiang;  Luo, Xin;  Shang, Mingsheng   |  收藏  |  浏览/下载：27/0  |  提交时间：2022/08/22 Computational modeling  Sparse matrices  Big Data  Data models  Stochastic processes  Training  Software algorithms  Big data  latent factor analysis  generally multilayered structure  deep forest  multilayered extreme learning machine  randomized-learning  high-dimensional and sparse matrix  stochastic gradient descent  randomized model   Non-Negative Latent Factor Model Based on beta-Divergence for Recommender Systems 期刊论文  OAI收割 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：14/0  |  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   A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems 期刊论文  OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 7, 页码: 4285-4296 作者:  Wu, Di;  Luo, Xin;  Shang, Mingsheng;  He, Yi;  Wang, Guoyin   |  收藏  |  浏览/下载：11/0  |  提交时间：2021/08/20 Big data  deep model  high-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  recommender system (RS)   An Instance-Frequency-Weighted Regularization Scheme for Non-Negative Latent Factor Analysis on High-Dimensional and Sparse Data 期刊论文  OAI收割 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：17/0  |  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   An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems 期刊论文  OAI收割 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：10/0  |  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)   Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 4, 页码: 1737-1749 作者:  Liu, Zhigang;  Luo, Xin;  Wang, Zidong   |  收藏  |  浏览/下载：63/0  |  提交时间：2021/05/17 Manganese  Convergence  Computational modeling  Learning systems  Analytical models  Sparse matrices  Big Data  Big data  convergence  high-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  learning system  neural networks  nonnegative LF (NLF) analysis  single LF-dependent nonnegative and multiplicative update (SLF-NMU)   Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data 期刊论文  OAI收割 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：12/0  |  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   Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems 期刊论文  OAI收割 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   |  收藏  |  浏览/下载：11/0  |  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   A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model 期刊论文  OAI收割 NEUROCOMPUTING, 2021, 卷号: 427, 页码: 29-39 作者:  Li, Jinli;  Yuan, Ye;  Ruan, Tao;  Chen, Jia;  Luo, Xin   |  收藏  |  浏览/下载：11/0  |  提交时间：2021/03/17 Big data  Stochastic gradient descent  Proportional integral derivation  PID controller  High-dimensional and sparse matrix  Latent factor analysis   An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences 期刊论文  OAI收割 IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13 作者:  Shang, Mingsheng;  Yuan, Ye;  Luo, Xin;  Zhou, MengChu   |  收藏  |  浏览/下载：9/0  |  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)