Relevant bibliographies by topics / Online Tensor Robust Principal Component Analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Online Tensor Robust Principal Component Analysis'

Author: Grafiati
Published: 11 December 2022
Last updated: 31 July 2025

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

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Online Tensor Robust Principal Component Analysis"

1

Wijnen, Michael. "Online Tensor Robust Principal Component Analysis." Thesis, 2018. http://hdl.handle.net/1885/170630.

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Tensor Robust Principal Component Analysis (TRPCA) is a procedure for recovering a data structure that has been corrupted by noise. In this thesis, a proof (inspired by Lu et al. (2018)) is given that TRPCA successfully performs this operation. An online optimisation algorithm to perform this procedure for p-dimensional tensors is proposed (based on a similar algorithm for the 3-dimensional case from Z. Zhang, Liu, Aeron, & Vetro (2016)). The required tensor identities to apply a proof of convergence (similar to the approach of Feng, Xu, & Yan (2013)
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Book chapters on the topic "Online Tensor Robust Principal Component Analysis"

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Liu, Yipeng, Jiani Liu, Zhen Long, and Ce Zhu. "Robust Principal Tensor Component Analysis." In Tensor Computation for Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74386-4_6.

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Xu, Chao, Hao Tan, Qingrong Feng, Yue Zhang, and Jianjun Wang. "Tensor Robust Principal Component Analysis with Hankel Structure." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8487-5_9.

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Srakar, Andrej. "Approximate Bayesian Algorithm for Tensor Robust Principal Component Analysis." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16427-9_1.

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Sun, Xiao, and Haitao Yin. "Multi-modal 3-D Medical Image Fusion Based on Tensor Robust Principal Component Analysis." In Image and Graphics Technologies and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6033-4_3.

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Conference papers on the topic "Online Tensor Robust Principal Component Analysis"

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Xu, Honghui, Yueqian Quan, Chuangjie Fang, and Jianwei Zheng. "Robust Principal Component Analysis via High-Order Self-Learning Transform Tensor Nuclear Norm." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687617.

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Aidini, Anastasia, Grigorios Tsagkatakis, and Panagiotis Tsakalides. "Quantized Tensor Robust Principal Component Analysis." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053151.

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Chen, Longxi, Yipeng Liu, and Ce Zhu. "Robust Tensor Principal Component Analysis in All Modes." In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018. http://dx.doi.org/10.1109/icme.2018.8486550.

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Ge, Weimin, Jinxiang Li, and Xiaofeng Wang. "Robust Tensor Principal Component Analysis Based on F-norm." In 2020 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2020. http://dx.doi.org/10.1109/icma49215.2020.9233820.

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Tan, Hao, Jianjun Wang, and Weichao Kong. "Deep Plug-and-Play for Tensor Robust Principal Component Analysis." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096713.

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Cai, HanQin, Zehan Chao, Longxiu Huang, and Deanna Needell. "Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00026.

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Ganyi Tang, Guifu Lu, Zhongqun Wang, and Yukai Xie. "Robust tensor principal component analysis by Lp-norm for image analysis." In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924765.

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Ju, Yakun, Lin Qi, Hao Fan, Liang Lu, and Junyu Dong. "Photometric stereo via random sampling and tensor robust principal component analysis." In Ninth International Conference on Graphic and Image Processing, edited by Hui Yu and Junyu Dong. SPIE, 2018. http://dx.doi.org/10.1117/12.2302425.

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Li, Tao, and Jinwen Ma. "T-SVD Based Non-convex Tensor Completion and Robust Principal Component Analysis." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412248.

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Van Luong, Huynh, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, and Andre Kaup. "Compressive online robust principal component analysis with multiple prior information." In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2017. http://dx.doi.org/10.1109/globalsip.2017.8309163.

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