Fast and Communication-efficient Distributed Pca

This paper focuses on principal components analysis (PCA), which involves estimating the principal subspace of a data covariance matrix, in the age of big data. Massively large datasets often require storage across multiple machines, which precludes the use of cen-tralized PCA solutions. While a number of distributed solutions to the PCA problem have been proposed recently, convergence guarantees and/or communications overhead of these solutions remain a concern. With an eye towards communications efficiency, this paper introduces two variants of a distributed PCA algorithm termed distributed Sanger's algorithm (DSA). Principal subspace estimation using both variants of DSA is communication efficient because of its one time-scale nature. In addition, theoretical guarantees are provided for the asymptotic convergence of basic DSA to the principal subspace, while its "accelerated" variant is numerically shown to have faster convergence than the state-of-the-art.