Selecting signature optical emission spectroscopy variables using sparse principal component analysis
Selecting signature optical emission spectroscopy variables using sparse principal component analysis is a scholarly work, published in 2008. The main subjects of the publication include variable, breath test, political representation, variance, mathematics, Sparse PCA, artificial intelligence, spectroscopy, computer science, Component analysis, graph dimension, signature, feature selection, pattern recognition, principal component analysis, chemometrics, and data mining. The 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.