首页|期刊导航|自动化学报（英文版）|Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
自动化学报（英文版）2021，Vol.8Issue(4)：796-805,10.DOI:10.1109/JAS.2020.1003533
Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
摘要
关键词
High-dimensional and sparse matrix/L1-norm/L2-norm/latent factor model/recommender system/smooth L1-normKey words
High-dimensional and sparse matrix/L1-norm/L2-norm/latent factor model/recommender system/smooth L1-norm引用本文复制引用
Di Wu,Xin Luo..Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data[J].自动化学报（英文版）,2021,8(4):796-805,10.基金项目
This work was supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337),in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013),in part by the Chinese Academy of Sciences"Light of West China"Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences,and by Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027).Recommended by Associate Editor Shangce Gao.(Corresponding author:Xin Luo.) （61702475,61772493,61902370,62002337）
