Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices

By John Wright, Arvind Ganesh, Shankar R. Rao, Yi Ma. arXiv.org, 2009

363 citations recorded by the source corpus.

This paper has been withdrawn due to a critical error near equation (71). This error causes the entire argument of the paper to collapse. Emmanuel Candes of Stanford discovered the error, and has suggested a correct analysis, which will be reported in a separate publication.

Subfields: Analytical Chemistry, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/13fb2d7aef8b6352e99c3ccd268fc139c34cf657

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In Analytical Chemistry

In Computational Mechanics

In Signal Processing

Citations in the corpus

Recovering Low-Rank Matrices From Few Coefficients in Any Basis (2009, background): “I NTRODUCTION We consider the problem of efficiently recovering a lowrank matrix from a small number of expansion coefficients with respect to some basis in the space of matrices.”
A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion (2009, methodology): “For outliers, a suitable algorithm would be to minimize the l 1 -norm of the errors instead of the l 2 -norm [11, 32].”
Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification (2017, neutral): “6 shows how our proposed background subtraction is efficient for camera-trap sequences by comparing the result of different dynamic scene challenges with other methods [9] [6].”
Matrix Completion from Noisy Entries (2009, background): “A number of iterative schemes to solve the convex optimization problem appeared soon thereafter [ 6, 7, 8] (also see [9] for a generalization).”