|Title:||Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso|
|Authors:||Fujiwara, Koichi https://orcid.org/0000-0002-2929-0561 (unconfirmed)|
Kano, Manabu https://orcid.org/0000-0002-2325-1043 (unconfirmed)
|Author's alias:||藤原, 幸一|
Input variable selection
Near infrared spectroscopy
|Journal title:||ISA Transactions|
|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.|
|Rights:||© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
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|Appears in Collections:||Journal Articles|
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