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Robust principal component analysis?

Authors:

Emmanuel J. Candès,

John WrightAuthors Info & Claims

Journal of the ACM (JACM), Volume 58, Issue 3

Article No.: 11, Pages 1 - 37

https://doi.org/10.1145/1970392.1970395

Published: 09 June 2011 Publication History

Abstract

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the ℓ₁ norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

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Zou SWang XYuan TZeng KLi GXie X(2025)Underwater moving target detection using online robust principal component analysis and multimodal anomaly detectionThe Journal of the Acoustical Society of America10.1121/10.0034831157:1(122-136)Online publication date: 10-Jan-2025
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Index Terms

Robust principal component analysis?
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis
        Robust regression

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Reviews

Reviewer: Jan De Beule

Consider a matrix M , such that M = L 0 + S 0, where L 0 is supposed to have low rank, and S 0 is supposed to be sparse. An interesting question, also motivated by numerous applications described in the paper, is whether one can efficiently reconstruct L 0 and S 0 if only M is given. The authors describe a procedure, called principal component pursuit (PCP), that recovers L 0 and S 0 exactly, under certain assumptions. One theorem clearly states the procedure, together with its necessary assumptions. The paper contains not only a mathematical description of PCP, but also a section describing numerical experiments using real-life data. Online Computing Reviews Service

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Published In

cover image Journal of the ACM

Journal of the ACM Volume 58, Issue 3

May 2011

122 pages

ISSN:0004-5411

EISSN:1557-735X

DOI:10.1145/1970392

Issue’s Table of Contents

Copyright © 2011 ACM.

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Publication History

Published: 09 June 2011

Accepted: 01 February 2011

Revised: 01 February 2011

Received: 01 January 2010

Published in JACM Volume 58, Issue 3

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https://doi.org/10.1016/B978-0-44-329238-5.00026-3
Zou SWang XYuan TZeng KLi GXie X(2025)Underwater moving target detection using online robust principal component analysis and multimodal anomaly detectionThe Journal of the Acoustical Society of America10.1121/10.0034831157:1(122-136)Online publication date: 10-Jan-2025
https://doi.org/10.1121/10.0034831
Kou ZWang JJia YLiu BGeng X(2025)Instance-Dependent Inaccurate Label Distribution LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332987036:1(1425-1437)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3329870
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