Improvement of variables interpretability in kernel PCA

Improvement of variables interpretability in kernel PCA

Abstract Background Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of principal component analysis is...

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Bibliographic Details
Main Authors: Mitja Briscik, Marie-Agnès Dillies, Sébastien Déjean
Format: Article
Language:English
Published: BMC 2023-07-01
Series:BMC Bioinformatics
Subjects:
Kernel PCA
Relevant variables
Unsupervised learning
Kernel methods
Online Access:https://doi.org/10.1186/s12859-023-05404-y

Internet

https://doi.org/10.1186/s12859-023-05404-y

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