Random consensus robust PCA
type: Item
instance of: scholarly article
title: Random consensus robust PCA
zbMATH Open document ID: 1384.62188
DOI: 10.1214/17-EJS1377SI
author: Daniel Pimentel-Alarcón
author: Robert D. Nowak
published in: Electronic Journal of Statistics
publication date: 2018-01-12T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1513306872
review text: The robust principal component analysis (RPCA) represents a problem of principal component analysis where every column in the data matrix \(M\) may have grossly corrupted entries. In this manuscript the authors introduce an algorithm for RPCA, the so-called random consensus algorithm for RPCA (R2PCA). The aim of this algorithm is to identify canonical projections which are used to recover subspace spanned by the columns of a low-rank matrix \(L\) of the matrix \(M\), where \(M=L+S\) and \(S\) is sparse. The algorithm is presented in its most basic setting, and a possible generalization to noisy settings is considered. At the end of the paper some extensive experiments are presented and the introduced model is compared with state-of-the-art methods.
reviewed by: Miroslav M. Ristić
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62G35
zbMATH DE Number: 6825045
zbMATH Keywords: principal component analysis
zbMATH Keywords: random sample consensus
zbMATH Keywords: random consensus algorithm for RPCA
MaRDI profile type: Publication
OpenAlex ID: W2772796212
description: scientific article; zbMATH DE number 6825045