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

doi:10.1109/JAS.2020.1003533

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
	Di Wu; Xin Luo
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:4 页码:796-805
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 L₂-norm-oriented Loss to represent an HiDS matrix, i.e., they sum the errors between observed data and predicted ones with L₂-norm. Yet L₂-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 L₁-norm-oriented latent factor (SL-LF) model. Its main idea is to adopt smooth L₁-norm rather than L₂-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.
关键词	High-dimensional and sparse matrix L₁-norm L₂-norm latent factor model recommender system smooth L₁-norm
DOI	10.1109/JAS.2020.1003533
引用统计	被引频次：32[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43948
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Di Wu,Xin Luo. 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	Di Wu,&Xin Luo.(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	Di Wu,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.

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