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Related Experiment Videos

Opening the kernel of kernel partial least squares and support vector machines.

G J Postma¹, P W T Krooshof, L M C Buydens

¹Radboud University Nijmegen, Institute for Molecules and Materials, Analytical Chemistry, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands. g.postma@science.ru.nl

Analytica Chimica Acta

|October 4, 2011

Summary

This study introduces a novel method to visualize variable contributions in kernel partial least squares (KPLS) and support vector regression (SVR) models. The technique successfully identifies important variables and their influence in complex data regression.

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Area of Science:

Machine Learning
Chemometrics
Data Mining

Background:

Kernel partial least squares (KPLS) and support vector regression (SVR) are widely used for complex non-linear data regression.
These methods map data to higher dimensions, often losing original variable contribution information.

Purpose of the Study:

To develop a method for retrieving and visualizing variable contributions in KPLS and SVR models.
To enhance the interpretability of complex regression models.

Main Methods:

Utilizing trajectory visualization with pseudo-samples representing original variables.
Applying the method to synthetic and real benchmark datasets.

Main Results:

The proposed method successfully identified important variables in both linear and non-linear regression models.
Variable contributions were visualized through corresponding linear or non-linear trajectories.
Results were validated by comparison with ordinary PLS regression and by rebuilding models with selected variables.

Conclusions:

The developed method effectively retrieves and visualizes variable contributions in kernel-based regression models.
This enhances model interpretability and aids in variable selection for complex datasets.