Distributed algorithm for principal component analysis based on power method and average consensus algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Principal component analysis is one of the most important methods of multivariate analysis, and has been applied in a wide range of fields such as statistical analysis, machine learning, pattern recognition, signal processing, and communication. Recently, using the idea of multi-agent networks, distributed algorithms for principal component analysis have been proposed for the case where the data matrix is partitioned either row-wise or column-wise. In this paper, considering the case where the data matrix is partitioned both row-wise and column-wise, we propose a new algorithm that allows a multi-agent network to perform principal component analysis in a distributed manner. We also verify its validity by numerical experiments. The proposed algorithm is based on the power method for principal component analysis and the average consensus algorithm.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020
EditorsYinglin Wang, Yanghua Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-21
Number of pages6
ISBN (Electronic)9781728170862
DOIs
Publication statusPublished - Dec 18 2020
Event7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020 - Shanghai, China
Duration: Dec 18 2020Dec 20 2020

Publication series

NameProceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020

Conference

Conference7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020
CountryChina
CityShanghai
Period12/18/2012/20/20

Keywords

  • Average consensus algorithm
  • Power method
  • Principal component analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing
  • Control and Optimization

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