Tensor Robust Principal Component Analysis

Tensor Robust Principal Component Analysis (TRPCA) aims to decompose a tensor—a multi-dimensional array—into a low-rank component representing the underlying structure and a sparse component representing noise or outliers. Current research focuses on improving the efficiency and accuracy of TRPCA algorithms, particularly for higher-order tensors and by incorporating spatial-temporal regularizations or non-convex penalties to better handle real-world data complexities. These advancements leverage techniques like tensor singular value decomposition (t-SVD), Tucker and Tensor Train decompositions, and Bayesian or deep learning frameworks, leading to improved performance in applications such as background subtraction, data clustering, and visual data recovery. The resulting improvements in robustness and scalability are significant for various data analysis tasks involving high-dimensional data.

Papers

November 10, 2023

Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization
Jianan Liu, Chunguang Li
Tensor Factorization Low Rank Tensor Tensor Robust Principal Component Analysis SVD Framework

September 27, 2023

Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction
Basit Alawode, Sajid Javed
Robust Principal Component Analysis Background Subtraction Tensor Robust Principal Component Analysis

July 18, 2023

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation
Tong Wu
Individual Representation Tensor Data Tensor Based LOw Rank Robust Clustering Tensor Robust Principal Component Analysis Tensor Recovery Task SVD Framework

May 6, 2023

Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption
HanQin Cai, Zehan Chao, Longxiu Huang, Deanna Needell
Low Rank Tensor Robust Principal Component Analysis Tensor Recovery CUR Decomposition Tensor Robust Principal Component Analysis

December 21, 2022

Deep Unfolded Tensor Robust PCA with Self-supervised Learning
Harry Dong, Megna Shah, Sean Donegan, Yuejie Chi
Self Supervised Learning Deep Unfolding Robust Principal Component Analysis Tensor Robust Principal Component Analysis

November 4, 2022

Tensor Robust PCA with Nonconvex and Nonlocal Regularization
Xiaoyu Geng, Qiang Guo, Shuaixiong Hui, Ming Yang, Caiming Zhang
General Nonconvex Robust Principal Component Analysis Tensor Robust Principal Component Analysis

September 28, 2022

Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis
Libin Wang, Yulong Wang, Shiyuan Wang, Youheng Liu, Yutao Hu, Longlong Chen, Hong Chen
Nuclear Norm Tensor Robust Principal Component Analysis

June 18, 2022

Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
Harry Dong, Tian Tong, Cong Ma, Yuejie Chi
Gradient Based Low Rank Tensor Robust Principal Component Analysis Tensor Robust Principal Component Analysis Factor Tensor

December 20, 2021

Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train
Yuning Qiu, Guoxu Zhou, Zhenhao Huang, Qibin Zhao, Shengli Xie
Hybrid Model Tensor Data Tensor Train Robust Principal Component Analysis Tucker Decomposition Tensor Robust Principal Component Analysis Tensor Recovery Task

Tensor Robust Principal Component Analysis

Papers

Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization

Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction

Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation

Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption

Deep Unfolded Tensor Robust PCA with Self-supervised Learning

Tensor Robust PCA with Nonconvex and Nonlocal Regularization

Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis

Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent

Efficient Tensor Robust PCA under Hybrid Model of Tucker and Tensor Train