.png){kind=icon}
返回
Fast and Secure Distributed Nonnegative Matrix Factorization
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2020.2985964.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its high cost on large matrices. On the other hand, the privacy issue of NMF over federated data is worthy of attention, since NMF is prevalently applied in image and text analysis which may involve leveraging privacy data (e.g, medical image and record) across several parties (e.g., hospitals). In this paper, we study the acceleration and security problems of distributed NMF. First, we propose a distributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems with a convergence guarantee. For the second problem, we show that DSANLS with modification can be adapted to the security setting, but only for one or limited iterations. Consequently, we propose four efficient distributed NMF methods in both synchronous and asynchronous settings with a security guarantee. We conduct extensive experiments on several real datasets to show the superiority of our proposed methods. The implementation of our methods is available at https://github.com/qianyuqiu79/DSANLS.
Keyword:
Servers
Distributed databases
Linear systems
Convergence
Data privacy
Manganese
Elbow
Distributed nonnegative matrix factorization
matrix sketching
privacy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
分享收藏Evolution and development of the tetrapod auditory system: an organ of Corti‐centric perspective
OAAI
OAAI分享收藏MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix FactorizationMpi-faun: 基于MPI的交替更新非负矩阵分解框架OAAI
OAAI分享收藏
查看更多内容