Fast, scalable and geo-distributed PCA for big data analytics
Abstract
Principal Component Analysis (PCA) is a widely popular technique for reducing the dimensionality of a dataset. Interestingly, when dimensions of the dataset grow too large, existing state-of-the-art methods for PCA face scalability issue due to the explosion of intermediate data. Moreover, in a geographically distributed environment where most of today’s data are originally generated, these methods require unnecessary data transmissions as they...
Paper Details
Title
Fast, scalable and geo-distributed PCA for big data analytics
Published Date
May 1, 2021
Journal
Volume
98
Pages
101710 - 101710
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