TY - JOUR
T1 - Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications
AU - Luo, Xin
AU - Zhou, Mengchu
AU - Li, Shuai
AU - Hu, Lun
AU - Shang, Mingsheng
N1 - Funding Information:
Manuscript received July 25, 2018; revised October 5, 2018 and November 18, 2018; accepted January 16, 2019. Date of publication February 27, 2019; date of current version April 15, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61772493 and Grant 91646114, in part by FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant 119/2014/A3, in part by the Chongqing Research Program of Technology Innovation and Application under Grant cstc2017rgzn-zdyfX0020, Grant cstc2017zdcy-zdyf0554, and Grant cstc2017rgzn-zdyf0118, in part by the Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group under Grant cstc2017kjrc-cxcytd0149, in part by the Chongqing Overseas Scholars Innovation Program under Grant cx2017012 and Grant cx2018011, and in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences. This paper was recommended by Associate Editor W. X. Zheng. (Xin Luo and Lun Hu are co-first authors.) (Corresponding authors: Xin Luo; MengChu Zhou.) X. Luo is with the School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China, also with the Chongqing Engineering Research Center of Big Data Application for Smart Cities, Chinese Academy of Sciences, Chongqing 400714, China, and also with the Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China (e-mail: luoxin21@dgut.edu.cn).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Alternating-direction-method of multipliers
KW - high-dimensional and sparse matrix
KW - industrial application
KW - non-negative latent factor analysis
KW - recommender system
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U2 - 10.1109/TCYB.2019.2894283
DO - 10.1109/TCYB.2019.2894283
M3 - Article
C2 - 30835233
AN - SCOPUS:85083905835
SN - 2168-2267
VL - 50
SP - 1844
EP - 1855
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 5
M1 - 8654191
ER -