@inproceedings{3e51242196dd4da68e0599ba31161382,
title = "Distributed Principal Component Analysis Based on Randomized Low-Rank Approximation",
abstract = "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.",
keywords = "dimension reduction, Distributed PCA, low rank approximation, randomized methods",
author = "Xinjue Wang and Jie Chen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2020 ; Conference date: 21-08-2020 Through 23-08-2020",
year = "2020",
month = aug,
day = "21",
doi = "10.1109/ICSPCC50002.2020.9259484",
language = "英语",
series = "ICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSPCC 2020 - IEEE International Conference on Signal Processing, Communications and Computing, Proceedings",
}