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 a valid nonlinear alternative to tackle the nonlinearity of biological sample spaces. This paper proposes a novel methodology to obtain a data-driven feature importance based on the kernel PCA representation of the data.

Results

The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The ac

SUBMITTER: Briscik M 

PROVIDER: S-EPMC10337068 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature