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Poster

Recovery Guarantees for Distributed-OMP

Chen Amiraz · Robert Krauthgamer · Boaz Nadler

MR1 & MR2 - Number 23
[ ]
Thu 2 May 8 a.m. PDT — 8:30 a.m. PDT

Abstract:

We study distributed schemes for high-dimensional sparse linear regression, based on orthogonal matching pursuit (OMP). Such schemes are particularly suited for settings where a central fusion center is connected to end machines, that have both computation and communication limitations. We prove that under suitable assumptions, distributed-OMP schemes recover the support of the regression vector with communication per machine linear in its sparsity and logarithmic in the dimension. Remarkably, this holds even at low signal-to-noise-ratios, where individual machines are unable to detect the support. Our simulations show that distributed-OMP schemes are competitive with more computationally intensive methods, and in some cases even outperform them.

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