A modification of canonical variates analysis to handle highly collinear multivariate data

By Lars Nørgaard, Rasmus Bro, Frank Westad, Søren Balling Engelsen. 2006

93 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1d5426cc73f2e3b26b8b608e8c2bf6c59b80fe16

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. Rasmus Bro
2. Davide Ballabio
3. Thomas Skov
4. R. Leardi
5. Evrim Acar
6. C. Cordella
7. Bülent Yener
8. Tamara G. Kolda

In Biomedical Engineering

1. Rasmus Bro
2. R. Leardi

In Computational Theory and Mathematics

1. Davide Ballabio
2. Thomas Skov
3. Rasmus Bro
4. R. Leardi
5. M. Vidal
6. José Manuel Amigo

Citations in the corpus

• Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques (2008, background): “ECVA has been recently proposed as a new classifier able to cope with collinear high-dimensional data [17].”
• A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, unknown): “[27,98–106].”