Probability-weighted tensor robust PCA with CP decomposition for hyperspectral image restoration

Zhang, Aiyi; Liu, Fulai; Du, Ruxu

doi:10.1016/j.sigpro.2023.109051

Cited by 6 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adopting tensor representations and methodologies produces superior results when extracting multidimensional structural features compared with matrix-based approaches. Despite the presence of noise or sparse outliers, this approach to robust tensor data recovery has demonstrated its effectiveness across fields such as SAR imaging [29], hyperspectral image compression [30,31], video sequence background subtraction, image and video denoising [32,33], and wireless communication [34]. With the vast amounts of multidimensional data produced by large-scale heterogeneous IoT systems, tensor-based PCAs effectively reveal correlations among multiple attributes.…”

Section: Noise and Outlier Reduction Using Robust Principal Component...mentioning

confidence: 99%

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Vo,

Nguyen,

Yoo

2024

Applied Sciences

View full text Add to dashboard Cite

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 component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.

show abstract