Distributed Robust Principal Component Analysis
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Abstract (arXiv)
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
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References · 5
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
- 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
- 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
- 2020Fast algorithms for robust principal component analysis with an upper bound on the rankcited by 2
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