Incremental QR-based tensor-train decomposition for industrial big data

doi:10.19682/j.cnki.1005-8885.2021.0003

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (1): 10-23.doi: 10.19682/j.cnki.1005-8885.2021.0003

• Artificial Intelligence • 上一篇下一篇

Incremental QR-based tensor-train decomposition for industrial big data

陈彦萍¹,靳晓东²,夏虹²,王忠民¹

1. 西安邮电学院
2. 西安邮电大学

收稿日期:2020-05-12 修回日期:2020-08-09 出版日期:2021-02-28 发布日期:2021-03-28
通讯作者: 夏虹 E-mail:xiahong@xupt.edu.cn
基金资助:
This work was supported by the Science and Technology Project in Shaanxi Province of China (2019ZDLGY07-08), the

Natural Science Foundation Research Program of Shaanxi Province, China.

Incremental QR-based tensor-train decomposition for industrial big data

#br#

1. School of Computer Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Received:2020-05-12 Revised:2020-08-09 Online:2021-02-28 Published:2021-03-28
Contact: hong Xia E-mail:xiahong@xupt.edu.cn
Supported by:
This work was supported by the Science and Technology Project in Shaanxi Province of China (2019ZDLGY07-08), the

Natural Science Foundation Research Program of Shaanxi Province, China.

摘要/Abstract

摘要：

Industrial big data was usually multi-source, heterogeneous, and deeply intertwined. It had a wide range of data sources, high data dimensions, and strong data correlation. In order to effectively analyze and process streaming industrial big data generated by edge computing, it was very important to provide an effective real-time incremental data method. However, in the process of incremental processing, industrial big data incremental computing faced the challenges of dimensional disaster, repeated calculations, and the explosion of intermediate results. Therefore, in order to solve the above problems effectively, a QR-based tensor-train (TT) decomposition (TTD) method and a QR-based incremental TTD (QRITTD) method were proposed. This algorithm combined the incremental QR-based decomposition algorithm with an approximate singular value decomposition ( SVD) algorithm and had good scalability. In addition, the computational complexity, space complexity, and approximation error analysis were analyzed in detail. The effectiveness of the three algorithms of QRITTD, non-incremental TTD (NITTD), and TT rank-1 (TTr1) SVD (TTr1SVD)were verified by comparison. Experimental results show that the SVD QRITTD method has better performance under the premise of ensuring the same tensor size.

关键词:

tensor-train decomposed| incremental processing| edge computing| industrial big data

Abstract:

Key words: tensor-train decomposed| incremental processing| edge computing| industrial big data

Chen Yanping, Jin Xiaodong, Xia Hong, Wang Zhongmin. Incremental QR-based tensor-train decomposition for industrial big data[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(1): 10-23.

参考文献

1. Kuang L W, Hao F, Yang L T, et al. A tensor-based approach for big data representation and dimensionality reduction. IEEE Transactions on Emerging Topics in Computin, 2014, 2(3): 280-291.

2. Ur Rehman M H, Yaqoob I, Salah K, et al. The role of big data analytics in industrial internet of things. Future Generation Computer Systems, 2019, 99: 247-259.

3. Wang C. Industrial big data software architecture and core components. Software and Integrated Circuits, 2019, (9): 50-51 (in Chinese).

4. Sun W L, Zhang X D, Xiong Z H et al. Intelligent manufacturing and its key technologies. Journal of Xinjiang University: Natural Science Edition, 2019, 36(4): 379-386 (in Chinese).

5. He W T, Shao C. The development and challenges of industrial big data analysis technology. Information and Control, 2018, 47(4): 398-410 (in Chinese).

6. Yu Y, Yi D J, Tang Z, et al. A regression prediction model based on incremental iteration for big industrial data. Proceedings of the IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SMARTCITY/DSS’19), 2019, Aug 10-12, Zhangjiajie, China. Piscataway, NJ, USA: IEEE, 2019: 1886-1893.

7. Zhao Y L, Yang L T, Zhang R H, et al. A tensor-based multiple clustering approach with its applications in automation systems. IEEE Transactions on Industrial Informatics, 2018, 14(1): 283-291.

8. Kaur D, Aujla G S, Kumar N, et al. Tensor-based big data management scheme for dimensionality reduction problem in smart grid systems: SDN perspective. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(10): 1985-1998.

9. Veganzones M A, Cohen J E, Farias R C, et al. Nonnegative tensor CP decomposition of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2577-2588.

10. Wang M Y, Song Y. Tensor decompositions via two-mode higher-order SVD (HOSVD). Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS’17), 2017, Apr 20-22, Fort Lauderdale, FL, USA. 2017: 614-622.

11. Hinrich J L, Mørup M. Probabilistic tensor train decomposition. Proceedings of the 27th European Signal Processing Conference (EUSIPCO’19), 2019, Sept 2-6, Coruna, Spain. Piscataway, NJ, USA: IEEE, 2019: 5p.

12. Li P, Chen Z K, Yang L T, et al. Deep convolutional computation model for feature learning on big data in Internet of things. IEEE Transactions on Industrial Informatics, 2018, 14(2): 790-798.

13. Li P, Chen Z K, Yang L T, et al. An incremental deep convolutional computation model for feature learning on industrial big data. IEEE Transactions on Industrial Informatics, 2019, 15(3): 1341-1349.

14. Zhang Q C, Zhu C S, Yang L T, et al. An incremental CFS algorithm for clustering large data in industrial Internet of things. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1193-1201.

15. Zhao Y L, Yang L T, Sun J Y. Privacy-preserving tensor-based multiple clusterings on cloud for industrial IoT. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2372-2381.

16. Kuang L W, Yang L T, Wang X K, et al. A tensor-based big data model for QoS improvement in software defined networks. IEEE Network, 2016, 30(1): 30-35.

17. Wu J, Dong M X, Ota K, et al. Big data analysis-based secure cluster management for optimized control plane in software-defined networks. IEEE Transactions on Network and Service Management, 2018, 15(1): 27-38.

18. Cui J, Lu Q H, Zhong H, et al. A load-balancing mechanism for distributed SDN control plane using response time. IEEE Transactions on Network and Service Management, 2018, 15(4): 1197-1206.

19. Holtz S, Rohwedder T, Schneider R. The alternating linear scheme for tensor optimization in the tensor train format. SIAM Journal on Scientific Computing, 2012, 34(2): A683-A713.

20. Cichocki A. Tensor networks for big data analytics and large-scale optimization problems. arXiv preprint, arxiv:1407.3124. 2014.

21. Gorodetsky A, Karaman S, Marzouk Y. High-dimensional stochastic optimal control using continuous tensor decompositions. The International Journal of Robotics Research, 2018, 37(2/3): 340-377.

22. Chen Z M, Batselier K, Suykens J A K, et al. Parallelized tensor train learning of polynomial classifiers. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4621-4632.

23. Bengua J A, Phien H O, Tuan H D, et al. Efficient tensor completion for color image and video recovery: Low-rank tensor train. IEEE Transactions on Image Processing, 2016, 26(5): 2466-2479.

24. Eduardo C, Abtin R, Denis Z. A tensor-train accelerated solver for integral equations in complex geometries. Journal of Computational Physics, 2017, 334: 145-169.

25. Martin C D M. Tensor decompositions workshop discussion notes. Palo Alto, CA, USA: American Institute of Mathematics, 2004: 1-27.

26. Henry E R, Hofrichter J. Singular value decomposition: Application to analysis of experimental data. Methods in Enzymology, 1992, 210: 129-192.

27. Volkov V G, Dem’yanov D N. Application of matrix decompositions for matrix canonization. Computational Mathematics and Mathematical Physics, 2019, 59(11): 1759-1770.

28. Oseledets I V. Tensor-train decomposition. SIAM Journal on Scientific Computing, 2011, 33(5): 2295-2317.

29. Batselier K, Liu H T, Wong N. A constructive algorithm for decomposing a tensor into a finite sum of orthonormal rank-1 terms. SIAM Journal on Matrix Analysis and Applications, 2014, 36(3), 1315-1337.

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Incremental QR-based tensor-train decomposition for industrial big data

Incremental QR-based tensor-train decomposition for industrial big data

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