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Journal articles on the topic 'Online Tensor Robust Principal Component Analysis'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

You, Yanan, Rui Wang, and Wenli Zhou. "An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition." Remote Sensing 12, no. 16 (2020): 2582. http://dx.doi.org/10.3390/rs12162582.

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The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor dec

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2

Feng, Lanlan, Yipeng Liu, Longxi Chen, Xiang Zhang, and Ce Zhu. "Robust block tensor principal component analysis." Signal Processing 166 (January 2020): 107271. http://dx.doi.org/10.1016/j.sigpro.2019.107271.

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3

Yang, Jing-Hua, Xi-Le Zhao, Teng-Yu Ji, Tian-Hui Ma, and Ting-Zhu Huang. "Low-rank tensor train for tensor robust principal component analysis." Applied Mathematics and Computation 367 (February 2020): 124783. http://dx.doi.org/10.1016/j.amc.2019.124783.

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4

Lee, Geunseop. "Accelerated Tensor Robust Principal Component Analysis via Factorized Tensor Norm Minimization." Applied Sciences 15, no. 14 (2025): 8114. https://doi.org/10.3390/app15148114.

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In this paper, we aim to develop an efficient algorithm for the solving Tensor Robust Principal Component Analysis (TRPCA) problem, which focuses on obtaining a low-rank approximation of a tensor by separating sparse and impulse noise. A common approach is to minimize the convex surrogate of the tensor rank by shrinking its singular values. Due to the existence of various definitions of tensor ranks and their corresponding convex surrogates, numerous studies have explored optimal solutions under different formulations. However, many of these approaches suffer from computational inefficiency pr

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5

Lu, Canyi, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, and Shuicheng Yan. "Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 4 (2020): 925–38. http://dx.doi.org/10.1109/tpami.2019.2891760.

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6

Luan, Yujie, and Wei Jiang. "Tensor Robust Principal Component Analysis via Hybrid Truncation Norm." OALib 09, no. 10 (2022): 1–22. http://dx.doi.org/10.4236/oalib.1109412.

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7

唐, 开煜. "The Nonconvex Framework for Robust Tensor Principal Component Analysis." Modeling and Simulation 13, no. 04 (2024): 4171–79. http://dx.doi.org/10.12677/mos.2024.134378.

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8

Cai, Shuting, Qilun Luo, Ming Yang, Wen Li, and Mingqing Xiao. "Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation." Applied Sciences 9, no. 7 (2019): 1411. http://dx.doi.org/10.3390/app9071411.

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Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as a non-convex regularization to approximate the low-rank component. Compared to various convex regularization, this new configuration not only can better capture the tensor rank but also provides a simplified approach. An optimization process is conducted via tensor singular decomposition and an eff

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9

费, 靖斯. "Tensor Robust Principal Component Analysis via Non-Convex Rank Approximation." Advances in Applied Mathematics 09, no. 10 (2020): 1815–20. http://dx.doi.org/10.12677/aam.2020.910210.

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10

杨, 枥皓. "Tensor Robust Principal Component Analysis Based on Truncated Nuclear Norm." Artificial Intelligence and Robotics Research 09, no. 02 (2020): 64–73. http://dx.doi.org/10.12677/airr.2020.92008.

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栾, 育洁. "Tensor Robust Principal Component Analysis Based on Hybrid Truncation Norm." Advances in Applied Mathematics 11, no. 10 (2022): 7373–79. http://dx.doi.org/10.12677/aam.2022.1110783.

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12

王, 颖. "Extended Tensor Robust Principal Component Analysis Model and Its Application." Pure Mathematics 13, no. 08 (2023): 2378–87. http://dx.doi.org/10.12677/pm.2023.138246.

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13

Song, Xiaoji, Tao Liu, Deliang Xiang, and Yi Su. "GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis." Remote Sensing 11, no. 8 (2019): 984. http://dx.doi.org/10.3390/rs11080984.

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The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that diff

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14

Gao, Kaixin, and Zheng-Hai Huang. "Tensor Robust Principal Component Analysis via Tensor Fibered Rank and \({\boldsymbol{{l_p}}}\) Minimization." SIAM Journal on Imaging Sciences 16, no. 1 (2023): 423–60. http://dx.doi.org/10.1137/22m1473236.

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15

Hu, Yue, Jin-Xing Liu, Ying-Lian Gao, Sheng-Jun Li, and Juan Wang. "Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method." Complexity 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/6136245.

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In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differenti

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16

Nie, Yongming, Linsen Chen, Hao Zhu, Sidan Du, Tao Yue, and Xun Cao. "Graph-regularized tensor robust principal component analysis for hyperspectral image denoising." Applied Optics 56, no. 22 (2017): 6094. http://dx.doi.org/10.1364/ao.56.006094.

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17

Liu, Yipeng, Longxi Chen, and Ce Zhu. "Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix." IEEE Journal of Selected Topics in Signal Processing 12, no. 6 (2018): 1378–89. http://dx.doi.org/10.1109/jstsp.2018.2873142.

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18

Huang, Zhang, Jun Feng, and Wei Li. "Efficient Tensor Robust Principal Analysis via Right-Invertible Matrix-Based Tensor Products." Axioms 14, no. 2 (2025): 99. https://doi.org/10.3390/axioms14020099.

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In this paper, we extend the definition of tensor products from using an invertible matrix to utilising right-invertible matrices, exploring the algebraic properties of these new tensor products. Based on this novel definition, we define the concepts of tensor rank and tensor nuclear norm, ensuring consistency with their matrix counterparts, and derive a singular value thresholding (*L,R SVT) formula to approximately solve the subproblems in the alternating direction method of multipliers (ADMM), which is integral to our proposed tensor robust principal component analysis (*LR TRPCA) algorithm

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19

Xiao, Wei, Xiaolin Huang, Fan He, Jorge Silva, Saba Emrani, and Arin Chaudhuri. "Online Robust Principal Component Analysis With Change Point Detection." IEEE Transactions on Multimedia 22, no. 1 (2020): 59–68. http://dx.doi.org/10.1109/tmm.2019.2923097.

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20

Xu, Xiaoshuang, Ruixue Gao, Yu Qing, Jun Feng, Zeyu Zeng, and Maozhi Wang. "Hyperspectral image mixed noise removal via tensor robust principal component analysis with tensor-ring decomposition." International Journal of Remote Sensing 44, no. 5 (2023): 1556–78. http://dx.doi.org/10.1080/01431161.2023.2187720.

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21

Mohaoui, Souad, Abdelilah Hakim, and Said Raghay. "Smooth tensor robust principal component analysis with application to color image recovery." Digital Signal Processing 123 (April 2022): 103390. http://dx.doi.org/10.1016/j.dsp.2022.103390.

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22

Sun, Weiwei, Gang Yang, Jiangtao Peng, and Qian Du. "Lateral-Slice Sparse Tensor Robust Principal Component Analysis for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters 17, no. 1 (2020): 107–11. http://dx.doi.org/10.1109/lgrs.2019.2915315.

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23

Liu, Yipeng, Tengteng Liu, Jiani Liu, and Ce Zhu. "Smooth robust tensor principal component analysis for compressed sensing of dynamic MRI." Pattern Recognition 102 (June 2020): 107252. http://dx.doi.org/10.1016/j.patcog.2020.107252.

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24

Jiang, Mingfeng, Qiannan Shen, Yang Li, et al. "Improved robust tensor principal component analysis for accelerating dynamic MR imaging reconstruction." Medical & Biological Engineering & Computing 58, no. 7 (2020): 1483–98. http://dx.doi.org/10.1007/s11517-020-02161-5.

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25

Van Luong, Huynh, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, and Andre Kaup. "Compressive Online Robust Principal Component Analysis via $n$ - $\ell_1$ Minimization." IEEE Transactions on Image Processing 27, no. 9 (2018): 4314–29. http://dx.doi.org/10.1109/tip.2018.2831915.

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26

Hong, Bin, Long Wei, Yao Hu, Deng Cai, and Xiaofei He. "Online robust principal component analysis via truncated nuclear norm regularization." Neurocomputing 175 (January 2016): 216–22. http://dx.doi.org/10.1016/j.neucom.2015.10.052.

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27

Han, Guang, Jinkuan Wang, and Xi Cai. "Background subtraction based on modified online robust principal component analysis." International Journal of Machine Learning and Cybernetics 8, no. 6 (2016): 1839–52. http://dx.doi.org/10.1007/s13042-016-0562-7.

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28

Lee, HyeungIll, and JungWoo Lee. "Online update techniques for projection based Robust Principal Component Analysis." ICT Express 1, no. 2 (2015): 59–62. http://dx.doi.org/10.1016/j.icte.2015.09.003.

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29

Jia, Xixi, Xiangchu Feng, Weiwei Wang, Hua Huang, and Chen Xu. "Online Schatten quasi-norm minimization for robust principal component analysis." Information Sciences 476 (February 2019): 83–94. http://dx.doi.org/10.1016/j.ins.2018.10.003.

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30

Zhang, Daiwei, Rounak Dey, and Seunggeun Lee. "Fast and robust ancestry prediction using principal component analysis." Bioinformatics 36, no. 11 (2020): 3439–46. http://dx.doi.org/10.1093/bioinformatics/btaa152.

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Abstract Motivation Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computat

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31

Hu, Yue, Fangsen Cui, Yifan Zhao, Fucai Li, Shuai Cao, and Fu-zhen Xuan. "Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection." Mechanical Systems and Signal Processing 204 (December 2023): 110761. http://dx.doi.org/10.1016/j.ymssp.2023.110761.

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32

Wang, Yuxia, and Qingjie Zhao. "Robust object tracking via online Principal Component–Canonical Correlation Analysis (P3CA)." Signal, Image and Video Processing 9, no. 1 (2013): 159–74. http://dx.doi.org/10.1007/s11760-013-0430-9.

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33

Rajesh, G., and Ashvini Chaturvedi. "Data Reconstruction in Heterogeneous Environmental Wireless Sensor Networks Using Robust Tensor Principal Component Analysis." IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 539–50. http://dx.doi.org/10.1109/tsipn.2021.3105795.

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34

Xu, Zhi, Jing-Hua Yang, Chuan-long Wang, Fusheng Wang, and Xi-hong Yan. "Tensor robust principal component analysis with total generalized variation for high-dimensional data recovery." Applied Mathematics and Computation 483 (December 2024): 128980. http://dx.doi.org/10.1016/j.amc.2024.128980.

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35

Gao, Kaixin, Zheng-Hai Huang, and Yang Xu. "Tensor Robust Principal Component Analysis Based on a Two-Layer Tucker Rank Minimization Model." SIAM Journal on Imaging Sciences 18, no. 2 (2025): 1522–61. https://doi.org/10.1137/24m1691788.

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36

Vo, Hanh Hong-Phuc, Thuan Minh Nguyen, and Myungsik Yoo. "Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things." Applied Sciences 14, no. 10 (2024): 4239. http://dx.doi.org/10.3390/app14104239.

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Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal

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37

Zhang, Yan, Yuyi Shao, Jinyue Shen, et al. "Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total variation." Applied Optics 60, no. 16 (2021): 4916. http://dx.doi.org/10.1364/ao.421081.

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38

Liu, Hongyi, Hanyang Li, Zebin Wu, and Zhihui Wei. "Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation." Remote Sensing 12, no. 14 (2020): 2264. http://dx.doi.org/10.3390/rs12142264.

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Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tens

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39

Sun, Li, and Bing Song. "Data Recovery Technology Based on Subspace Clustering." Scientific Programming 2022 (July 20, 2022): 1–6. http://dx.doi.org/10.1155/2022/1920933.

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High-dimensional data usually exist asymptotically in low-dimensional space. In this study, we mainly use tensor t-product as a tool to propose new algorithms in data clustering and recovery and verify them on classical data sets. This study defines the “singular values” of tensors, adopts a weighting strategy for the singular values, and proposes a tensor-weighted kernel norm minimization robust principal component analysis method, which is used to restore low-probability low-rank third-order tensor data. Experiments on synthetic data show that in the recovery of strictly low-rank data, the t

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Liu, Yuanyuan, Leslie Ying, Weitian Chen та ін. "Accelerating the 3D T1ρ mapping of cartilage using a signal-compensated robust tensor principal component analysis model". Quantitative Imaging in Medicine and Surgery 11, № 8 (2021): 3376–91. http://dx.doi.org/10.21037/qims-20-790.

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41

Zou, Shaofeng, Xuyang Wang, Tao Yuan, Kaihui Zeng, Guolin Li, and Xiang Xie. "Underwater moving target detection using online robust principal component analysis and multimodal anomaly detection." Journal of the Acoustical Society of America 157, no. 1 (2025): 122–36. https://doi.org/10.1121/10.0034831.

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In shallow water, reverberation complicates the detection of low-intensity, variable-echo moving targets, such as divers. Traditional methods often fail to distinguish these targets from reverberation, and data-driven methods are constrained by the limited data on intruding targets. This paper introduces the online robust principal component analysis and multimodal anomaly detection (ORMAD) method to address these challenges. ORMAD efficiently performs online low-rank and sparse decomposition while utilizing unsupervised multimodal anomaly detection to enhance detection performance. The multim

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42

Ge, Mao, Yong Lv, and Yubo Ma. "Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis." IEEE Access 8 (2020): 178425–49. http://dx.doi.org/10.1109/access.2020.3027029.

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43

Tan, Huachun, Bin Cheng, Jianshuai Feng, Li Liu, and Wuhong Wang. "Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/914963.

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The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and theℓ1-norm. The problem is well structured in both the objective function and co

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44

Liu, Zhihao, Weiqi Jin, and Li Li. "Temporal Denoising of Infrared Images via Total Variation and Low-Rank Bidirectional Twisted Tensor Decomposition." Remote Sensing 17, no. 8 (2025): 1343. https://doi.org/10.3390/rs17081343.

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Temporal random noise (TRN) in uncooled infrared detectors significantly degrades image quality. Existing denoising techniques primarily address fixed-pattern noise (FPN) and do not effectively mitigate TRN. Therefore, a novel TRN denoising approach based on total variation regularization and low-rank tensor decomposition is proposed. This method effectively suppresses temporal noise by introducing twisted tensors in both horizontal and vertical directions while preserving spatial information in diverse orientations to protect image details and textures. Additionally, the Laplacian operator-ba

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45

López-Bueno, David, Quynh Anh Pham, Gabriel Montoro, and Pere L. Gilabert. "Independent Digital Predistortion Parameters Estimation Using Adaptive Principal Component Analysis." IEEE Transactions on Microwave Theory Techniques 66, no. 12 (2018): 5771–79. https://doi.org/10.1109/TMTT.2018.2870420.

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This paper presents an estimation/adaptation method based on the adaptive principal component analysis (APCA) technique to guarantee the identification of the minimum necessary parameters of a digital predistorter. The proposed estimation/adaptation technique is suitable for online field-programmable gate array or system on chip implementation. By exploiting the orthogonality of the resulting transformed matrix obtained with the APCA technique, it is possible to reduce the number of coefficients to be estimated which, at the same time, has a beneficial regularization effect by preventing ill-c

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46

Wang, Yulong. "Superposed Atomic Representation for Robust High-Dimensional Data Recovery of Multiple Low-Dimensional Structures." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15735–42. http://dx.doi.org/10.1609/aaai.v38i14.29502.

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This paper proposes a unified Superposed Atomic Representation (SAR) framework for high-dimensional data recovery with multiple low-dimensional structures. The data can be in various forms ranging from vectors to tensors. The goal of SAR is to recover different components from their sum, where each component has a low-dimensional structure, such as sparsity, low-rankness or be lying a low-dimensional subspace. Examples of SAR include, but not limited to, Robust Sparse Representation (RSR), Robust Principal Component Analysis (RPCA), Tensor RPCA (TRPCA), and Outlier Pursuit (OP). We establish t

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47

Zhang, Guiyu, Xianguo Tuo, Shuang Zhai, Xuemei Zhu, Lin Luo, and Xianglin Zeng. "Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM." Sensors 22, no. 4 (2022): 1654. http://dx.doi.org/10.3390/s22041654.

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Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors, and its results are inaccurate and unstable. This study developed a near-infrared (NIR) spectral characteristic extraction method based on a three-dimensional analysis space and establishes a high-accuracy qualitative identification model. First, the Norris derivative filtering algorithm was used in the pre-processing of the NIR spectrum to obtai

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48

Plourde, Alexandre P., and Michael G. Bostock. "Relative moment tensors and deep Yakutat seismicity." Geophysical Journal International 219, no. 2 (2019): 1447–62. http://dx.doi.org/10.1093/gji/ggz375.

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SUMMARY We introduce a new relative moment tensor (MT) inversion method for clusters of nearby earthquakes. The method extends previous work by introducing constraints from S-waves that do not require modal decomposition and by employing principal component analysis to produce robust estimates of excitation. At each receiver, P and S waves from each event are independently aligned and decomposed into principal components. P-wave constraints on MTs are obtained from a ratio of coefficients corresponding to the first principal component, equivalent to a relative amplitude. For S waves we produce

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49

Liang, Peidong, Chentao Zhang, Habte Tadesse Likassa, and Jielong Guo. "New Robust Tensor PCA via Affine Transformations and L 2,1 Norms for Exact Tubal Low-Rank Recovery from Highly Corrupted and Correlated Images in Signal Processing." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–14. http://dx.doi.org/10.1155/2022/3002348.

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In this latest work, the Newly Modified Robust Tensor Principal Component Analysis (New RTPCA) using affine transformation and L 2,1 norms is proposed to remove the outliers and heavy sparse noises in signal processing. This process is done by decomposing the original data matrix as the low-rank heavy sparse noises. The determination of the potential variables is casted as constrained convex optimization problem, and the Alternating Direction Method of Multipliers (ADMM) method is considered to reduce the computational loads in an iterative manner. The simulation results validate the effective

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50

Zhang, Zhao, Cheng Ding, Zhisheng Gao, and Chunzhi Xie. "ANLPT: Self-Adaptive and Non-Local Patch-Tensor Model for Infrared Small Target Detection." Remote Sensing 15, no. 4 (2023): 1021. http://dx.doi.org/10.3390/rs15041021.

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Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse

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