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Title: Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso
Authors: Fujiwara, Koichi  kyouindb  KAKEN_id  orcid (unconfirmed)
Kano, Manabu  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 藤原, 幸一
Keywords: Soft-sensor design
Input variable selection
Group Lasso
Spectral clustering
Near infrared spectroscopy
Issue Date: Sep-2015
Publisher: Elsevier Ltd.
Journal title: ISA Transactions
Volume: 58
Start page: 367
End page: 379
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
The full-text file will be made open to the public on 1 September 2017 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1016/j.isatra.2015.04.007
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