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Analysis of PCA Algorithms in Distributed Environments
[article]
2015
arXiv
pre-print
Classical machine learning algorithms often face scalability bottlenecks when they are applied to large-scale data. Such algorithms were designed to work with small data that is assumed to fit in the memory of one machine. In this report, we analyze different methods for computing an important machine learing algorithm, namely Principal Component Analysis (PCA), and we comment on its limitations in supporting large datasets. The methods are analyzed and compared across two important metrics:
arXiv:1503.05214v2
fatcat:5irvd6qdmvae5lsmi7eluqf5da