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W2000248374 abstract "We study the robust PCA problem of reliably recovering a low-rank signal matrix from a signal-plus-noise-plus-outliers matrix. We analytically characterize the extent to which the singular vectors of the signal-plus-noise-plus-outliers matrix can be degraded by outliers and discuss why a recently proposed method for robust PCA that exploits outlier sparsity to improve low-rank estimation will produce suboptimal low-rank matrix estimates in the presence of noise. Next, we propose a new iterative algorithm for robust PCA that utilizes an optimal, data-driven low-rank matrix estimator (OptShrink). Finally, we show that the proposed approach yields superior background subtraction on a computer vision dataset." @default.
W2000248374 created "2016-06-24" @default.
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W2000248374 date "2014-06-01" @default.
W2000248374 modified "2023-09-25" @default.
W2000248374 title "Improved Robust PCA using low-rank denoising with optimal singular value shrinkage" @default.
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W2000248374 doi "https://doi.org/10.1109/ssp.2014.6884563" @default.
W2000248374 hasPublicationYear "2014" @default.
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