Sparse and Structured Function-on-Function Quality Predictive Modeling by Hierarchical Variable Selection and Multitask Learning

被引:3
|
作者
Wang, Kai [1 ,2 ]
Tsung, Fugee [3 ]
机构
[1] Xi An Jiao Tong Univ, Sch Management, Xian 215123, Peoples R China
[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 215123, Peoples R China
[3] Hong Kong Univ Sci & Technol, Dept Ind Engn & Decis Analyt, Hong Kong, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Functional data analysis (FDA); functional regression (FR); regularization method; sparse learning; structure penalty;
D O I
10.1109/TII.2020.3041830
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Modern manufacturing industries are often featured with a data-rich environment. The real-time behaviors of process variables can be completely recorded as multiple various signal signatures, and the geometric quality of finished products can be thoroughly characterized by their 2-D surface data. Learning the relationship between such signal predictors and surface responses, where the input and output are no longer the conventional scalar variables but are in fact both functions in the time domain and spatial domain, respectively, is critical for quality prediction in many applications nowadays. To this end, this article proposes a novel sparse and structured function-on-function regression ((SSFR)-R-2) model, where a hierarchical variable selection is developed to identify informative signals and further screen significant elements within the selected signals, and a multitask learning is devised to exploit the smoothness nature of surface response and the similarity structure among a series of subregression tasks. Our (SSFR)-R-2 model is concisely formulated as a convex problem with an efficient iterative algorithm derived to obtain the global optimum. Moreover, our quality prediction can be performed dynamically during an ongoing manufacturing process when only partial observations of the signal predictors are available. The superiority of our proposed method is validated by numerical simulations and a real case study in the semiconductor industry.
引用
收藏
页码:6720 / 6730
页数:11
相关论文
共 18 条
  • [1] VARIABLE SELECTION FOR MULTIPLE FUNCTION-ON-FUNCTION LINEAR REGRESSION
    Cai, Xiong
    Xue, Liugen
    Cao, Jiguo
    STATISTICA SINICA, 2022, 32 (03) : 1435 - 1465
  • [2] Specifying a hierarchical mixture of experts for hydrologic modeling: Gating function variable selection
    Jeremiah, Erwin
    Marshall, Lucy
    Sisson, Scott A.
    Sharma, Ashish
    WATER RESOURCES RESEARCH, 2013, 49 (05) : 2926 - 2939
  • [3] A Novel Two-step Sparse Learning Approach for Variable Selection and Optimal Predictive Modeling
    Liu, Yiren
    Qin, S. Joe
    IFAC PAPERSONLINE, 2022, 55 (07): : 57 - 64
  • [4] Quantile function regression and variable selection for sparse models
    Yoshida, Takuma
    CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2021, 49 (04): : 1196 - 1221
  • [5] glmgraph: an R package for variable selection and predictive modeling of structured genomic data
    Chen, Li
    Liu, Han
    Kocher, Jean-Pierre A.
    Li, Hongzhe
    Chen, Jun
    BIOINFORMATICS, 2015, 31 (24) : 3991 - 3993
  • [6] High dimensional variable selection through group Lasso for multiple function-on-function linear regression: A case study in PM10 monitoring
    Evangelista, Adelia
    Acal, Christian
    Aguilera, Ana M.
    Sarra, Annalina
    Di Battista, Tonio
    Palermi, Sergio
    ENVIRONMETRICS, 2025, 36 (01)
  • [7] Polynomial Response Surface based on basis function selection by multitask optimization and ensemble modeling
    Yong Zhao
    Siyu Ye
    Xianqi Chen
    Yufeng Xia
    Xiaohu Zheng
    Complex & Intelligent Systems, 2022, 8 : 1015 - 1034
  • [8] Polynomial Response Surface based on basis function selection by multitask optimization and ensemble modeling
    Zhao, Yong
    Ye, Siyu
    Chen, Xianqi
    Xia, Yufeng
    Zheng, Xiaohu
    COMPLEX & INTELLIGENT SYSTEMS, 2022, 8 (02) : 1015 - 1034
  • [9] A Sparse Modeling Method Based on Reduction of Cost Function in Regularized Forward Selection
    Hagiwara, Katsuyuki
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2014, E97D (01): : 98 - 106
  • [10] APPLICATION OF MULTIVARIATE CLUSTER, DISCRIMINATE FUNCTION, AND STEPWISE REGRESSION-ANALYSES TO VARIABLE SELECTION AND PREDICTIVE MODELING OF SPERM CRYOSURVIVAL
    DAVIS, RO
    DROBNIS, EZ
    OVERSTREET, JW
    FERTILITY AND STERILITY, 1995, 63 (05) : 1051 - 1057