Paper Info
Title
Feature selection based on concurrent projection to latent structures for high dimensional spectra data
Abstract
Dimension reduction is one of the important issues for soft sensor model construction based on data-driven method; especially for the high dimensional spectra data. To select effective input features with clear physical interpretation and simple model structure is very necessary. In this paper, a new feature selection method based on concurrent projection to latent structures (CPLS) algorithm is proposed to select important input features for modeling spectra data effectively. The proposed method selects input features based on un-scaled data in terms of special characteristics of spectra data. A new modified simple sphere criterion (SSC) is used to select the input features relative to output data with only one latent variable. The final soft sensor model is constructed with the scaled selected data by using PLS algorithm. Near-infrared (NIR) spectra and mechanical frequency spectra data are used to validate the proposed method. Simulation results show that the proposed approach has better generalization performance than that of the former proposed PLS-based one.
Year
DOI
Venue
2016
10.1109/ICInfA.2016.7831828
2016 IEEE International Conference on Information and Automation (ICIA)
Keywords
Field
DocType
Feature selection,Concurrent projection to latent structures (CPLS),High dimensional spectra data,Simple sphere criterion (SSC),Soft sensor model
Data modeling,Dimensionality reduction,Feature selection,Pattern recognition,Computer science,Soft sensor,Fault detection and isolation,Feature extraction,Latent variable,Artificial intelligence,Principal component analysis
Conference
ISBN
Citations 
PageRank 
978-1-5090-4103-9
0
0.34
References 
Authors
10
5
Name
Order
Citations
PageRank
Jian Tang1526148.30
Tianyou Chai22014175.55
S. Joe Qin310514.38
zhuo liu472.52
Wen Yu524652.12