Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

doi:10.1109/JAS.2020.1003533

	Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
	Wu, Di 1,2; Luo, Xin 1,2
刊名	IEEE-CAA JOURNAL OF AUTOMATICA SINICA
	2021-04-01
卷号	8 期号:4 页码:796-805
关键词	High-dimensional and sparse matrix L-1-norm L-2-norm latent factor model recommender system smooth L-1-norm
ISSN号	2329-9266
DOI	10.1109/JAS.2020.1003533
通讯作者	Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要	High-dimensional and sparse (HiDS) matrices commonly arise in various industrial applications, e.g., recommender systems (RSs), social networks, and wireless sensor networks. Since they contain rich information, how to accurately represent them is of great significance. A latent factor (LF) model is one of the most popular and successful ways to address this issue. Current LF models mostly adopt L-2-norm-oriented Loss to represent an HiDS matrix, i.e., they sum the errors between observed data and predicted ones with L-2-norm. Yet L-2-norm is sensitive to outlier data. Unfortunately, outlier data usually exist in such matrices. For example, an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users. To address this issue, this work proposes a smooth L-1- norm-oriented latent factor (SL-LF) model. Its main idea is to adopt smooth L-1- norm rather than L-2- norm to form its Loss, making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix. Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
资助项目	National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[61902370] ; National Natural Science Foundation of China[62002337] ; Natural Science Foundation of Chongqing, China[cstc2019jcyj-msxmX0578] ; Natural Science Foundation of Chongqing, China[cstc2019jcyjjqX0013] ; Chinese Academy of Sciences Light of West China Program ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Technology Innovation and Application Development Project of Chongqing, China[cstc2019jscx-fxydX0027]
WOS研究方向	Automation & Control Systems
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000628913100006
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/13212]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Luo, Xin
作者单位	1.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China
推荐引用方式 GB/T 7714	Wu, Di,Luo, Xin. Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2021,8(4):796-805.
APA	Wu, Di,&Luo, Xin.(2021).Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,8(4),796-805.
MLA	Wu, Di,et al."Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 8.4(2021):796-805.