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j.isatra.2015.04.007.pdf | 14.68 MB | Adobe PDF | View/Open |
Title: | Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso |
Authors: | Fujiwara, Koichi ![]() ![]() ![]() Kano, Manabu ![]() ![]() ![]() |
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 http://creativecommons.org/licenses/by-nc-nd/4.0/ 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'. 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。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/203543 |
DOI(Published Version): | 10.1016/j.isatra.2015.04.007 |
Appears in Collections: | Journal Articles |

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