Ma, Beibei and McLoone, Sean F. and Ringwood, John and Macgerailt, N. (2008) Selecting signature optical emission spectroscopy variables using sparse principal component analysis. In: 11th International Conference on Computer and Information Technology, 2008. ICCIT 2008. 10.1109/ICCITECHN.2008.4803104 . IEEE, pp. 14-19. ISBN 9781424421350

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Abstract

Principal component analysis (PCA) is a widely used technique in optical emission spectroscopy (OES) sensor data analysis for the low dimension representation of high dimensional datasets. While PCA produces a linear combination of all the variables in each loading, sparse principal component analysis (SPCA) focuses on using a subset of variables in each loading. Therefore, SPCA can be used as a key variable selection technique. This paper shows that, using SPCA to analyze 2046 variable OES data sets, the number of selected variables can be traded off against variance explained to identifying a subset of key wavelengths, with an acceptable level of variance explained. SPCArelated issues such as selection of the tuning parameter and the grouping effect are discussed.

Item Type:	Book Section
Keywords:	principal component analysis; data handling; infrared spectroscopy;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	8842
Depositing User:	Professor John Ringwood
Date Deposited:	21 Sep 2017 13:09
Publisher:	IEEE
Refereed:	Yes
Funders:	Enterprise Ireland
URI:	http://ieeexplore.ieee.org
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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