Assessing feature relevance in NPLS models by VIP

By S. Favilla, C. Durante, M. L. Vigni, M. Cocchi. 2013

124 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Biophysics

View Paper: https://www.semanticscholar.org/paper/3b2c97e250579015b4beaf6ef50a10769c5d5bc1

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. Tormod Næs
3. A. Biancolillo
4. Kristian Hovde Liland
5. I. Måge
6. Evrim Acar
7. C. Cordella
8. Bülent Yener

In Biomedical Engineering

1. Rasmus Bro
2. Tormod Næs
3. Kristian Hovde Liland

In Biophysics

1. Rasmus Bro

Citations in the corpus

• Variable selection in multi-block regression (2016, methodology): “The variable importance in projection (VIP) [15,17] is another model-based method widely used to select features.”