Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso

•A new variable selection method for a soft-sensor was developed.•The proposed variable selection methods are based on the correlation between variables and group Lasso.•Parameter tuning in the proposed method is easier than conventional methods.•The proposed method was applied to soft-sensor design of a pharmaceutical process and a chemical process.•The proposed method could reduce the number of input variables of a soft-sensor and improve its performance.

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.

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