Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (10): 134-143.doi: 10.11896/jsjkx.240800076

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sparsity Cooperated Correntropy Based Robust Principal Component Analysis

CHEN Ping1, LIU Kehan2, LIANG Zhengyou1, HU Qixing2, ZHANG Yuanpeng3,4   

  1. 1 School of Computer,Electronics and Information,Guangxi University,Nanning 530000,China
    2 Chengdu University of Information Technology,Chengdu 610225,China
    3 Department of Medical Informatics,Nantong University,Nantong,Jiangsu 226000,China
    4 Department of Health and Technology,Hong Kong Polytechnic University,Hong Kong 999077,China
  • Received:2024-08-14 Revised:2024-12-05 Online:2025-10-15 Published:2025-10-14
  • About author:CHEN Ping,born in 1986,postgra-duate.His main research interest is computer vision.
    LIANG Zhengyou,born in 1968,Ph.D,professor,is a member of CCF(No.16803M).His main research interests include computer vision and image processing.
  • Supported by:
    National Natural Science Foundation of China(12101090),Sichuan Provincial Science and Technology Department(2023NSFSC1425,2023NSFSC0071,2023NSFSC1362) and 2023 Chengdu University of Information Technology Science and Technology Innovation Capability Enhancement Plan Innovation Team Key Project(KYTD202322).

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Abstract: PCA is widely used in many applications but is sensitive to non-Gaussian noise.Many Robust PCA models have been proposed to handle this issue.However,these methods only can handle one type of noise,such as the impulse noise in the feature domain or the outliers in the sample domain.This paper proposes a novel RPCA model based on sparsity cooperated correntropy called SCPCA,which is robust against impulse noise and outlier simultaneously.Furthermore,an iterative algorithm is proposed to solve the proposed model based on the Fenchel conjugate and the accelerated BCU strategy.Extensive experiments on clustering,background reconstruction and face modelling have been conducted to evaluate the robustness of the proposed method.The results show that the proposed method outperforms the compared state of-the-art methods in most situations.

Key words: Robust principal component analysis,Correntropy,Background reconstruction,Face Modeling

CLC Number: 

  • TP181
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