论文标题
分布式强大的主成分分析
Distributed Robust Principal Component Analysis
论文作者
论文摘要
我们在分布式设置中研究了可靠的主成分分析（RPCA）问题。 RPCA的目的是在数据矩阵遭受总稀疏错误的损坏时，找到原始数据矩阵的基本低排名估计。先前的研究开发了RPCA算法，这些算法可提供快速收敛的稳定溶液。但是，由于使用SVD或大型矩阵乘法，这些算法通常很难扩展，并且无法分布分布。在本文中，我们提出了基于共识分解的第一个分布式强大的主分析算法，称为DCF-PCA。我们证明了DCF-PCA的收敛性并在各种问题设置上评估DCF-PCA
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
