In this article, an optimal data-driven approach is proposed to deal with the problem of
distribution independent fault detection (FD) for stochastic linear discrete-time systems. For …

Dimensionality reduction (DR) targets to learn low-dimensional representations for
improving discriminability of data, which is essential for many downstream machine learning …

The high dimensionality of real-life datasets is one of the biggest challenges in the machine
learning field. Due to the increased need for computational resources, the higher the …

Dimensionality reduction is a critical factor in processing high-dimensional datasets. The L1
norm-based Two-Dimensional Linear Discriminant Analysis (L1-2DLDA) is widely used for …

Rank minimization is widely used to extract low-dimensional subspaces. As a convex
relaxation of the rank minimization, the problem of nuclear norm minimization has been …

Welding processes significantly impact product quality as a crucial part of industrial
production. Due to the reflection, diversity, complexity, and minuteness of solder defects …

Dimension reduction is an effective technology to embed data with high dimension to lower
dimension space, where Linear Discriminant Analysis (LDA), one of representative methods …

Modern industrial systems are increasingly automated and highly integrated with the rapid
development of computer science, mechanical engineering, electronics and information …