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Distributed Principal Component Analysis with Limited Communication
Foivos Alimisis · Peter None Davies · Bart Vandereycken · Dan Alistarh

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @ None #None

We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.

Author Information

Foivos Alimisis (IST Austria)
Peter None Davies (University of Surrey)
Bart Vandereycken (Princeton University)
Dan Alistarh (IST Austria & NeuralMagic)

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