TY - GEN A1 - Gao, Yunlong A1 - Luo, Si-Zhe A1 - Pan, Jin-Yan A1 - Chen, Bai-Hua A1 - Zhang, Yi-Song T1 - Robust PCA Using Adaptive Probability Weighting T2 - Acta Automatica Sinica N2 - Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms. KW - Principle component analysis (PCA) KW - weighted principal component analysis (WPCA) KW - dimensionality reduction KW - robustness Y1 - 2021 U6 - https://doi.org/10.16383/j.aas.c180743 SN - 0254-4156 VL - 47 IS - 4 SP - 825 EP - 838 ER -