Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

For many modern applications in science and engineering, data are collected in a streaming
fashion carrying time-varying information, and practitioners need to process them with a …

Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …

Robust principal component analysis (RPCA) has been widely used for many data analysis
problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to …

Moving object detection is a fundamental step in various computer vision applications.
Robust principal component analysis (RPCA)-based methods have often been employed for …

The paper provides a specific perspective view on background subtraction for moving object
detection, as a building block for many computer vision applications, being the first relevant …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be …

Dynamic robust principal component analysis (PCA) refers to the dynamic (time-varying)
extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lie in a low …

L1-norm principal-component analysis (L1-PCA) is known to attain sturdy resistance against
faulty points (outliers) among the processed data. However, computing the L1-PCA of large …

This paper provides a comparative theoretical and experimental evaluation of solutions for
robust PCA and robust subspace tracking (dynamic RPCA) that rely on the sparse+ low-rank …