We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when the data matrix is subject to the corruption of gross sparse errors. Previous studies have developed RPCA algorithms that provide stable solutions with fast convergence. However, these algorithms are typically hard to scale and cannot be implemented distributedly, due to the use of either SVD or large matrix multiplication. In this paper, we propose the first distributed robust principal analysis algorithm based on consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA and evaluate DCF-PCA on various problem setting

翻译：我们在一个分布式环境中研究稳健的主要组成部分分析(RPCA)问题;RPCA的目标是,当数据矩阵受到严重稀少错误的腐蚀时,为原始数据矩阵找到一个基本的低级估计值;以前的研究已经发展出RPCA算法,提供了稳定、快速趋同的解决办法;然而,由于使用SVD或大型矩阵乘法,这些算法通常难以规模化,无法进行分配;在本文件中,我们提议了第一个基于协商一致的系数化的、称为DCF-PCA的分布式强健的主要分析算法。我们证明DCF-PCA的趋同,并评估DCF-PCA在各种问题设置方面的评估。