Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This
study introduces and investigates the use of kernel PCA for novelty detection. Training data …

This paper presents a method for learning a distance metric from relative comparison such
as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach, we …

A new fault detection and identification method based on kernel principal component
analysis (PCA) is described. In the past, numerous PCA-based statistical process monitoring …

Duplication of image regions is a common method for manipulating original images, using
typical software like Adobe Photoshop, 3DS MAX, etc. In this study, we propose a …

The peer group of an image pixel is a pixel similarity-based concept which has been
successfully used to devise image denoising methods. However, since it is difficult to define …

Given their key position in the process control industry, process monitoring techniques have
been extensively investigated by industrial practitioners and academic control researchers …

In practice, because of complex mechanism processes, such as heating process, volume
heterogeneity, and various chemical reaction characteristics, there is a nonlinear …

Accurate detection and identification of natural or intentional contamination events in a
drinking water pipe is critical to drinking water supply security and health risk management …

Linear regression is one of the most important and widely used techniques for data analysis.
However, sometimes people are not satisfied with it because of the following two limitations …