KMS Chongqing Institute of Green and Intelligent Technology, CAS
Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications | |
Luo, Xin1,2,3; Zhou, MengChu4,5; Li, Shuai6; Hu, Lun7; Shang, Mingsheng2,3 | |
2020-05-01 | |
摘要 | High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related industrial applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many industrial data. Moreover, existing models suffer from slow convergence rate. An alternating-direction-method of multipliers-based non-negative LF (AMNLF) model decomposes the task of non-negative LF analysis on an HiDS matrix into small subtasks, where each task is solved based on the latest solutions to the previously solved ones, thereby achieving fast convergence and high prediction accuracy for its missing data. This paper theoretically analyzes the characteristics of an AMNLF model, and presents detailed empirical studies regarding its performance on nine HiDS matrices from industrial applications currently in use. Therefore, its capability of addressing HiDS matrices is justified in both theory and practice. |
关键词 | Computational modeling Data models Sparse matrices Linear programming Training Convergence Analytical models Alternating-direction-method of multipliers high-dimensional and sparse matrix industrial application non-negative latent factor analysis recommender system |
DOI | 10.1109/TCYB.2019.2894283 |
发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS |
ISSN | 2168-2267 |
卷号 | 50期号:5页码:1844-1855 |
通讯作者 | Luo, Xin(luoxin21@dgut.edu.cn) ; Zhou, MengChu(zhou@njit.edu) |
收录类别 | SCI |
WOS记录号 | WOS:000528622000006 |
语种 | 英语 |