Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

Mingsheng Shang; Xin Luo; Zhigang Liu; Jia Chen; Ye Yuan; MengChu Zhou

doi:10.1109/JAS.2018.7511189

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 131-141

Mingsheng Shang, Xin Luo, Zhigang Liu, Jia Chen, Ye Yuan and MengChu Zhou, "Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 131-141, Jan. 2019. doi: 10.1109/JAS.2018.7511189

Citation:

Mingsheng Shang, Xin Luo, Zhigang Liu, Jia Chen, Ye Yuan and MengChu Zhou, "Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 131-141, Jan. 2019. doi: 10.1109/JAS.2018.7511189

Citation:

Mingsheng Shang, Xin Luo, Zhigang Liu, Jia Chen, Ye Yuan and MengChu Zhou, "Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 131-141, Jan. 2019. doi: 10.1109/JAS.2018.7511189

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

doi: 10.1109/JAS.2018.7511189

Mingsheng Shang^1
,,
Xin Luo^{1
,
,},
Zhigang Liu^1
,,
Jia Chen^2
,,
Ye Yuan^1
,,
MengChu Zhou^{3
,
,}

1.
Chongqing Engineering Research Center of Big Data Application for Smart Cities, and Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
2.
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
3.
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA

Funds: This work was supported in part by the National Natural Science Foundation of China (61772493; 91646114), Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020), and in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences. Recommended by Associate Editor Jian Yang

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Author Bio:
Mingsheng Shang received the B.E. degree in management in Sichuan Normal University in Chengdu, China in 1995, and Ph.D. degree in computer science from University of Electronic Science and Technology of China in Chengdu, China in 2007. He is currently a Professor at the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China. His research interests include complex network analysis and big data applications. (e-mail: msshang@cigit.ac.cn)

Xin Luo (M'14-SM'17) received the B.S. degree in computer science from the University of Electronic Science and Technology of China, Chengdu, China, in 2005, and the Ph.D. degree in computer science from Beihang University, Beijing, China, in 2011. In 2016, he joined the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China, where he is currently a Professor of computer science and engineering. His research interests include big data analysis and intelligent control. He has published more than 90 papers (including 30+ IEEE Transactions papers) in his related areas. (e-mail: luoxin21@cigit.ac.cn)

Zhigang Liu received the B.S. degree in geographical information system from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2013. He is currently pursuing an M.E. degree in computer technology at Chongqing University, Chongqing, China, and studying at the Chongqing institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China as an exchange scholar. His research interests include big data analysis and algorithm design for large scale data applications. (e-mail: liuzhigang@cigit.ac.cn)

Jia Chen received the B.S. degree in electronic information engineering from Hunan University, Hunan, China, in 2007. She is a doctoral candidate in the college of computer science and a faculty member in asset management department, Beihang University. Her research interests include big data analysis, social relationship and asset management. (e-mail: chenjia@buaa.edu.cn)

Ye Yuan received the B.S. degree in electronic information engineering and the M.S. degree in signal processing from the University of Electronic Science and technology, Chengdu, China, in 2010 and 2013, respectively. He is currently working toward the Ph.D. degree in computer science from Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China. His research interests include data mining, recommender system, intelligent computing, and their applications. (e-mail: yuanye@cigit.ac.cn)

MengChu Zhou (S'88-M'90-SM'93-F'03) received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, Beijing, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 800 publications including 12 books, 460+ journal papers (360+ in IEEE transactions), 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS), and Chinese Association of Automation (CAA). (e-mail: zhou@njit.edu)
Corresponding author: Xin Luo, e-mail: luoxin21@cigit.ac.cn; MengChu Zhou, e-mail: zhou@njit.edu
Received Date: 2018-02-05
Revised Date: 2018-04-09
Accepted Date: 2018-04-22

Abstract

Abstract

Latent factor (LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse (HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers, which may consume many iterations to achieve a local optima, resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor (RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly. Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data. I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.
- Big data,
- high-dimensional and sparse matrix,
- latent factor analysis,
- latent factor model,
- randomized learning

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References(48)

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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