Feature selection in principal component analysis of analytical data

By Q. Guo, W. Wu, D. Massart, C. Boucon, S. D. Jong. 2002

149 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Food Science

View Paper: https://www.semanticscholar.org/paper/d3704c2d4dfabd31fd68ad0b0a4cff95798b0b5e

How researchers see this paper

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

In Analytical Chemistry

1. Alejandro C. Olivieri
2. Héctor C. Goicoechea
3. Cynthia Rudin
4. Agustin Gutierrez-Galvez
5. Santiago Marco
6. Nicolaas (Klaas) M. Faber

In Biomedical Engineering

1. Alejandro C. Olivieri
2. Cynthia Rudin

In Food Science

1. Alejandro C. Olivieri
2. Cynthia Rudin
3. Agustin Gutierrez-Galvez
4. Santiago Marco
5. Nicolaas (Klaas) M. Faber

Citations in the corpus

• A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, methodology): “2008), but we focus on one proposed by Guo et al. (2002), which is similar to Krzanowski (1987).”
• A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy (2003, background): “tivity relationships (QSARs) [20], classification problems [21,22], selection of the best principal components for multivariate models [23], etc.”