Quantitative analysis of 16–17th century archaeological glass vessels using PLS regression of EPXMA and µ‐XRF data

By P. Lemberge, I. De Raedt, K. Janssens, F. Wei, P. van Espen. 2000

57 citations recorded by the source corpus.

Subfields: Archeology, Computer Vision and Pattern Recognition, Paleontology

View Paper: https://www.semanticscholar.org/paper/5d5d5df840768d0104244c0b2e9d4f9daa4904c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Archeology

1. Peter Filzmoser

In Computer Vision and Pattern Recognition

1. Mia Hubert
2. Peter J. Rousseeuw

Citations in the corpus

• ROBPCA: A New Approach to Robust Principal Component Analysis (2005, background): “3 Glass spectra Our third data set consists of EPXMA spectra over p = 750 wavelengths collected on 180 different glass samples (Lemberge et al. 2000).”
• Outlier identification in high dimensions (2008, result): “Relations between concentrations and spectra were investigated with partial least squares (PLS) in Lemberge et al. (2000), although these results were biased due to outliers.”
• High-Breakdown Robust Multivariate Methods (2008, methodology): “We illustrate the PCA outlier map on a dataset consisting of spectra of 180 archaeological glass pieces over p = 750 wavelengths (Lemberge et al., 2000).”