Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data
文献类型：期刊论文
| 作者 | Di Wu; Xin Luo |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2021 |
| 卷号 | 8期号:4页码:796-805 |
| 关键词 | High-dimensional and sparse matrix L1-norm L2-norm latent factor model recommender system smooth L1-norm |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2020.1003533 |
| 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 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43948]  |
| 专题 | 自动化研究所_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. |
入库方式： OAI收割
来源：自动化研究所
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