Online update techniques for projection based Robust Principal Component Analysis

Lee, Hyeungill; Lee, JungWoo

doi:10.1016/j.icte.2015.09.003

Cited by 5 publications

(2 citation statements)

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“…Poor data quality: To address problems related to poor data quality, such as missing or spurious observations, a class of techniques called robust PCA has been developed. Pioneering work by Walczak and Massart [143] and Xie et al [144] laid the foundation for this field, which was further developed with applications and extensions [145][146][147][148][149][150][151].…”

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confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.

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confidence: 99%

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

Melo,

Câmara,

Pinto

2024

Processes

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“…FARST (Ahn [4]) incPCP (Rodriguez and Wohlberg [310]) ROSETA (Mansour et al [255] incPCP-TI (Rodriguez and Wohlberg [312] ORPCA (Xu [413]) ORPCA-SGD (Song et al [352]) Projection RPCA (Lee and Lee [211]) OTNNR (Hong et al [153]) Real-time Algorithms CAQR (Anderson et al [8]) -Real Time pROST (Hage et al [319]) --Real time PCP (Pope et al [288]) LR Submatrix Recovery/Reconstruction (LRSRR) (Guo et al [126]) Real time inPCP (Rodriguez [307]) Real time incPCP-TI (Silva and Rodriguez [343]) Dealing with the challenges Noisy videos: Noisy videos: -Noisy videos: -OR-PCA with ARF (Javed et al [167]) RobustXray (Kumar et al [201]) RMAMC (Yang et al [419]) VBSE (Chen et al [66]) Bootstrapping: MAGRPCA (Javed et al [172]) Camera jitter:…”

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confidence: 99%

Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset

Bouwmans,

Sobral,

Javed

et al. 2015

Preprint

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Background/foreground separation is the first step in video surveillance system to detect moving objects. Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix. However, similar robust implicit or explicit decompositions can be made in the following problem formulations: Robust Non-negative Matrix Factorization (RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. These formulation problems differ from the implicit or explicit decomposition, the loss function, the optimization problem and the solvers. As the problem formulation can be NP-hard in its original formulation, and it can be convex or not following the constraints and the loss functions used, the key challenges concern the design of efficient relaxed models and solvers which have to be with iterations as few as possible, and as efficient as possible. In the application of background/foreground separation, constraints inherent to the specificities of the background and the foreground as the temporal and spatial properties need

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