Distributed Principal Component Analysis Based on Randomized Low-Rank Approximation

Xinjue Wang, Jie Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Distributed PCA aims to implement dimension reduction for data stored on multiple agents. The conventional distributed PCA encounters the bottleneck of computation when the dimension of 10-cal data is large. In this work, we propose a distributed PCA algorithm with local processing based on randomized methods for the star network topology (master-slave networks) with distributed row observations. Local matrix approximation with randomized methods allows us to accelerate the computation with an acceptable loss of precision significantly. The results of numerical experiments show that the proposed algorithm can achieve satisfactory decomposition results with much lower computational complexity.

源语言英语
主期刊名ICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781728172019
DOI
出版状态已出版 - 21 8月 2020
活动2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020 - Macau, 中国
期限: 21 8月 202023 8月 2020

出版系列

姓名ICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings

会议

会议2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020
国家/地区中国
Macau
时期21/08/2023/08/20

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