Bayesian optimization of multiscale kernel principal component analysis and its application to model Gas-to-liquid (GTL) process data

Abstract

Kernel methods map the data from original space into a higher-dimensional space in which linear methods are applied. In many applications, the inverse mapping is also important, and the pre-image of a feature vector must be found in the original space. Kernel principal component analysis (KPCA) based kernel density estimation (KDE) has been developed to solve this problem. However, the performance of the KPCA technique greatly depends on the choice of some parameters which can lead to poor modeling performance when these parameters are not well identified. Thus, fully Bayesian optimization KPCA (BOKPCA) is proposed to enhance the performance of the KPCA model. BOKPCA method aims to automatically select the best parameters of the KPCA model. Generally, kernel methods struggle to handle nonlinear data contaminated with high levels of noise. This is because the noise affects every principal component, making it challenging to mitigate its influence during the reconstruction step. Consequently, to further enhance the ability of KPCA and BOKPCA models, we propose to integrate multi-scale filtering with these two models. The efficiency of the proposed methods are evaluated using a simulated nonlinear process and real data generated from a bench-scale Fischer–Tropsch (FT) process.