Dec 18, 2020
Distributed algorithm for principal component analysis based on power method and average consensus algorithm
Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020
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- First page
- 16
- Last page
- 21
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1109/PIC50277.2020.9350816
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.
- Link information
- ID information
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- DOI : 10.1109/PIC50277.2020.9350816
- ISBN : 9781728170862
- SCOPUS ID : 85101687142