Robust PCA via Outlier Pursuit

NeurIPS 2010 Huan XuConstantine CaramanisSujay Sanghavi

Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers. Recent work has considered the setting where each point has a few arbitrarily corrupted components... (read more)

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