We propose “supervised principal component analysis (supervised PCA)”, a generalization
of PCA that is uniquely effective for regression and classification problems with high …

In this paper, a decentralized fault diagnosis approach of complex processes is proposed
based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by …

Based on near-infrared (NIR) spectroscopy, a new quantitative calibration algorithm, called
“Partial Least Squares Regression Residual Extreme Learning Machine (PLSRR-ELM)” …

New approaches are proposed for nonlinear process monitoring and fault diagnosis based
on kernel principal component analysis (KPCA) and kernel partial least analysis (KPLS) …

The paper presents a novel method for the extraction of facial features based on the Gabor-
wavelet representation of face images and the kernel partial-least-squares discrimination …

This paper introduces a novel multivariate regression approach based on kernel partial least
squares (KPLS) with orthogonal signal correction (OSC). OSC has been proposed as a data …

A new quantitative calibration method based on spectral differences between calibration
samples, named “moving window correlation coefficient differences partial least squares …

Projection to latent structure (PLS) is a well-known data-based approach widely used in
industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the …

In this paper, kernel partial least squares (KPLS) method is modified based on orthogonal
independent component analysis (O-ICA). Then it is applied to quality prediction of industrial …

In this paper, we propose a learning-based super resolution approach consisting of two
steps. The first step uses the kernel partial least squares (KPLS) method to implement the …