title: Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso.
abstract: Appropriate input variables have to be selected for building highly accurate soft sensor. A novel input variable selection method based on nearest correlation spectral clustering (NCSC) has been proposed, and it is referred to as NCSC-based variable selection (NCSC-VS). Although NCSC-VS can select appropriate input variables, a lot of parameters have to be tuned carefully for selecting proper variables. The present work proposes a new methodology for efficient input variable selection by integrating NCSC and group Lasso. The proposed NCSC-based group Lasso (NCSC-GL) can not only reduce the number of tuning parameters but also achieve almost the same performance as NCSC-VS. The usefulness of the proposed NCSC-GL is demonstrated through applications to soft sensor design for a pharmaceutical process and a chemical process.
authors.affiliation: Department of Systems Science, Kyoto University, Yoshida-Honmachi, Sakyoku, Kyoto 606-8501, Japan. Electronic address: fujiwara.koichi@i.kyoto-u.ac.jp.
authors.forename: Koichi
authors.initials: K
authors.lastname: Fujiwara
authors.affiliation: Department of Systems Science, Kyoto University, Yoshida-Honmachi, Sakyoku, Kyoto 606-8501, Japan.
authors.forename: Manabu
authors.initials: M
authors.lastname: Kano
mesh_terms: D000465:Algorithms; D002138:Calibration; D002617:Chemical Industry / Q000706:statistics & numerical data; D016000:Cluster Analysis; D003198:Computer Simulation; D004339:Drug Compounding / Q000706:statistics & numerical data; D004345:Drug Industry / Q000706:statistics & numerical data; D005030:Ethylenes / Q000737:chemistry; D016018:Least-Squares Analysis; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared
chemical_list: D005030:Ethylenes
keywords: Group Lasso; Input variable selection; Near infrared spectroscopy; Soft-sensor design; Spectral clustering