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Robust Principal Component Analysis Based On Trimming Around Affine Subspaces

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Figure 1: Influence function of the largest eigenvalue at P = N (0, diag(2, 1)) when α = 0 (left panel) and α = 0.01 (right panel).
Figure 2: Influence function of the eigenvector associated to the largest eigenvalue at P = N (0, diag(2, 1)) when α = 0 (left panel) and α = 0.01 (right panel).
Table 1: Finite sample efficiencies of the eigenvalues and eigenvectors of the trimmed PCA method w.r.t
Table 2: Finite sample efficiencies of the eigenvalues and eigenvectors of the trimmed PCA method w.r.t

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