Scalable Soft Sensor for Nonlinear Industrial Big Data via Bagging Stochastic Variational Gaussian Processes

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 8; pp. 7594 - 7602
• Main Authors: Zhu, Jinlin, Jiang, Muyun, Peng, Guohao, Yao, Le, Ge, Zhiqiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bagging; Big Data; Complexity theory; Data models; ensemble learning; feature selection; Gaussian process; Gaussian process (GP); Gaussian processes; Ground penetrating radar; Inference; Mathematical analysis; scalable soft sensor

Traditional Gaussian process regression suffers from the cubic complexity and excessive computation burdens for industrial big data. To get rid of such defect, this work proposes a scalable soft sensor called bagging stochastic variational GP regression (SVGPR). We first formulate the Gaussian proce...