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Input variable selection for PLS modeling using nearest correlation spectral clustering
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DOI：10.1016/j.chemolab.2012.08.007.png)
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Abstract
En 中文
Soft-sensors have been widely used for estimating product quality or other key variables, and partial least squares (PLS) regression is accepted as a useful technique for soft-sensor design. To achieve high estimation performance, it is important to select appropriate input or explanatory variables. The present work proposes a new systematic methodology to select input variables for PLS using nearest correlation spectral clustering (NCSC), which is a clustering method based on the correlation among variables. The proposed method, referred to as NCSC-based variable selection (NCSC-VS), clusters the variables into some variable classes by using NCSC, and selects a few variable classes according to their contribution to estimates. That is, the input variables are not selected individually but some variables that have similar correlation are selected together. The usefulness of the proposed NCSC-VS is demonstrated through an application to soft-sensor design for an industrial chemical process. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Soft-senor
Variable selection
Spectral clustering
Regression
Graph theory
Modeling
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