智能系统学报2025，Vol.20Issue(4)：822-837,16.DOI:10.11992/tis.202406003
融合低秩预分离与随机抖动机制的非凸型TRPCA算法
Nonconvex TRPCA algorithm combined with low-rank pre-separation and random jitter mechanism
摘要
Abstract
To address the issue of information extraction bias caused by uniform shrinkage of singular values in tensor robust principal component analysis(TRPCA)during low-rank structure recovery,this study considered treating singu-lar values differently,using a nonconvex weighted tensor Schatten-p norm(0＜p＜1)to analyze tensor data,which re-duced the penalty for singular values.In order to solve the problem of severe data damage that is difficult to recover,a low-rank pre-separation method was used to realize the pre-separation of the approximate low-rank component and the approximate sparse component.To enhance the correlation among high-order tensors while reducing the sensitivity of data to specific noise,the random jitter regularizer mechanism was proposed to optimize the selected random regions for the pre-separated components respectively,which constrained the complexity of the model by using the randomness of the noise information to regularize the algorithm.Finally,experiments were conducted on high-dimensional data recov-ery using different types of image datasets,including color images,MRI images,hyperspectral and multispectral images,and grayscale images.The results show that the proposed method significantly outperforms other TRPCA approaches in image recovery performance and maintains advantages even under severe data corruption.It effectively extracts princip-al component information while reducing dependence on specific noise patterns,demonstrating strong robustness and adaptability.This method can serve as a valuable reference for TRPCA-based image recovery applications.关键词
主成分分析/张量/图像去噪/图像处理/机器学习/计算机应用/信号处理/奇异值分解Key words
principal component analysis/tensor/image denoising/image processing/machine learning/computer ap-plication/signal processing/singular value decomposition分类
信息技术与安全科学引用本文复制引用
潘昱妍,张德,李壮举..融合低秩预分离与随机抖动机制的非凸型TRPCA算法[J].智能系统学报,2025,20(4):822-837,16.基金项目
国家自然科学基金项目(62271035) （62271035）
北京市自然科学基金项目(4232021). （4232021）
