TY - JOUR A1 - Schmitt, Andrew L. A1 - Zhu, Lei A1 - Schmeißer, Dieter A1 - Himpsel, Franz J. A1 - Jin, Song T1 - Metallic Single-Crystal CoSi Nanowires via Chemical Vapor Deposition of Single-Source Precursor Y1 - 2006 ER - TY - JOUR A1 - Schmitt, Andrew L. A1 - Bierman, Matthew J. A1 - Schmeißer, Dieter A1 - Himpsel, Franz J. A1 - Jin, Song T1 - Synthesis and Properties of Single-Crystal FeSi Nanowires Y1 - 2006 ER - TY - CHAP A1 - Kaliappan, Prabhu Shankar A1 - König, Hartmut A1 - Schmerl, Sebastian A1 - Dong, Jin Song A1 - Zhu, Huibiao T1 - Model-Driven Protocol Design Based on Component Oriented Modeling T2 - Formal methods and software engineering, proceedings 12th International Conference on Formal Engineering Methods, ICFEM 2010, Shanghai, China, November 17 - 19, 2010 Y1 - 2010 UR - www.springer.com/lncs SN - 978-3-642-16900-7 SP - 613 EP - 629 PB - Springer CY - Berlin [u.a.] ER - 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 -