面向工业大数据的多层增量特征提取方法

doi:10.3969/j.issn.1001-2400.2018.04.019

Abstract

Abstract:

We focus on the failure of Incremental Linear Discriminant Analysis in the case of a high-dimensional small sample in industrial big data. An improved multi-layer incremental feature extraction method for industrial big data is proposed to solve this problem which can reduce dimension effectively, and at the same time the variance information and discriminant information on the sample is kept as much as possible. First, the data streams are updated incrementally with the sliding window in real time, and its outliers are detected and filtered. Second, the incremental principal component analysis is made initially to extract the features of the data and the Fisher discriminant function is used to quantify the classification information contained in each principal element. Then the contribution and recognition ability of principal components are weighted by the entropy evaluation method to select the principal components. The selected principal components constitute a new feature space. Finally, Incremental Linear Discriminant Analysis is made to complete the second feature extraction and classification for the high-dimensional data. Experimental results indicate that the improved method could extract the features effectively in real-time and that the industrial data can be discriminated better.

Key words: industrial big data, high dimensional and small sample, feature extraction, incremental linear discriminant analysis, incremental principal component analysis, entropy method

WANG Xing;HUANG Xiaoyu;LIU Xuanpu;KONG Xianguang;NIU Meng. Multi-layer incremental feature extraction method for industrial big data[J].Journal of Xidian University, 2018, 45(4): 106-111.

References

［1］张敏. 复杂生产过程质量控制的智能方法研究［D］. 成都: 西南交通大学, 2013.
［2］孔宪光, 章雄, 马洪波, 等. 面向复杂工业大数据的实时特征提取方法［J］. 西安电子科技大学学报, 2016, 43(5): 70-74, 152.
KONG Xianguang, ZHANG Xiong, MA Hongbo, et al. Real Time Feature Extraction Method for Complex Industrial Big Data［J］. Journal of Xidian University, 2016, 43(5): 70-74, 152.
［3］单燕. 数据流降维算法研究［D］. 南京: 南京邮电大学, 2016.
［4］ KONG X, CHANG J, NIU M, et al. Research on Real Time Feature Extraction Method for Complex Manufacturing Big Data［J］. International Journal of Advanced Manufacturing Technology, 2016: 1-8.
［5］ JOSEPH A A, TOKUMOTO T, OZAWA S. Online Feature Extraction Based on Accelerated Kernel Principal Component Analysis for Data Stream［J］. Evolving Systems, 2016, 7(1): 15-27.
［6］ JAFFEL I, TAOUALI O, HARKAT M F, et al. Kernel Principal Component Analysis with Reduced Complexity for Nonlinear Dynamic Process Monitoring［J］. International Journal of Advanced Manufacturing Technology, 2017, 88(9/10/11/12): 3265-3279.
［7］ SHARMA A, PALIWAL K K. Linear Discriminant Analysis for the Small Sample Size Problem: an Overview［J］. International Journal of Machine Learning and Cybernetics, 2015, 6(3): 443-454.
［8］ YANG W, WU H. Regularized Complete Linear Discriminant Analysis［J］. Neurocomputing, 2014, 137(11): 185-191.
［9］ PALIWAL K K, SHARMA A. Improved Pseudoinverse Linear Discriminant Analysis Method for Dimensionality Reduction［J］. International Journal of Pattern Recognition and Artificial Intelligence, 2012, 26(1): 1250002.
［10］钟伟, 黄元亮, 郝真真, 等. 基于粒子群算法的LDA实现方法研究［J］. 计算机工程与应用, 2017, 53(1): 39-43.
ZHONG Wei, HUANG Yuanliang, HAO Zhenzhen, et al. Research on LDA Implementation Method Based on Particle Swarm Optimization［J］. Computer Engineering and Applications, 2017, 53(1): 39-43.
［11］赵越, 徐鑫, 乔利强. 张量线性判别分析算法研究［J］. 计算机技术与发展, 2014, 24(1): 73-76.
ZHAO Yue, XU Xin, QIAO Liqiang. Research on Linear Discriminant Analysis Algorithm of Tensor［J］. Computer Technology and Development, 2014, 24(1): 73-76.
［12］ ZENG X Q, LI G Z. Incremental Partial Least Squares Analysis of Big Streaming Data［J］. Pattern Recognition, 2014, 47(11): 3726-3735.
［13］ HOU M, CHAIB-DRAA B. Online Incremental Higher-order Partial Least Squares Regression for Fast Reconstruction of Motion Trajectories from Tensor Streams［C］//Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2016: 6205-6209.
［14］罗来正, 肖勇, 王宝瑞, 等. 熵值法在涂层老化指标权重确定中的应用［J］. 装备环境工程, 2017, 14(7): 70-73.
LUO Laizheng, XIAO Yong, WANG Baorui, et al. Application of Entropy Method in Weight Determining of Coating Aging Index［J］. Equipment Environmental Engineering, 2017, 14(7): 70-73.
［15］呼妮, 王勇. 一种改进的基于反k近邻的流数据离群点检测算法［J］. 计算机与现代化, 2016(8): 32-35.
HU Ni, WANG Yong. An Improved Stream Data Outlier Mining Algorithm Based on Reverse k-Nearest Neighbors［J］. Computer and Modernization, 2016(8): 32-35.

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Multi-layer incremental feature extraction method for industrial big data

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