Distributed Principal Component Analysis Based on Randomized Low-Rank Approximation
| Autor: | Xinjue Wang, Jie Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Star network
Computational complexity theory Computer science Computation Dimensionality reduction 020206 networking & telecommunications Low-rank approximation 02 engineering and technology Bottleneck Matrix (mathematics) Dimension (vector space) Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | ICSPCC |
| DOI: | 10.1109/icspcc50002.2020.9259484 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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