学術雑誌論文 Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso

Fujiwara, Koichi  ,  Kano, Manabu

58pp.367 - 379 , 2015-09 , Elsevier Ltd.
ISSN:0019-0578
NII書誌ID(NCID):AA00669225
内容記述
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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http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/203543/1/j.isatra.2015.04.007.pdf

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