Allerton 2015 Paper Abstract


Paper ThA1.4

Rahmani, Mostafa (University of Central Florida), Atia, George (University of Central Florida)

A Decentralized Approach to Robust Subspace Recovery

Scheduled for presentation during the Invited Session "Distributed Decision Making" (ThA1), Thursday, October 1, 2015, 09:30−09:50, Library

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Distributed Computation on Networks, Statistical Signal Processing, Sensor Networks


This paper considers subspace recovery in the presence of outliers in a decentralized setting. The intrinsic low-dimensional geometry of the data is exploited to substantially reduce the processing and communication overhead given limited sensing and communication resources at the sensing nodes. A small subset of the data is first selected. The data is embedded into a random low-dimensional subspace then forwarded to a central processing unit that runs a low-complexity algorithm to recover the subspace directly from the compressed data. We derive sufficient conditions on the compression and communication rates to successfully recover the subspace with high probability. It is shown that the proposed approach is robust to outliers and its complexity is independent of the dimension of the given data. The proposed algorithm provably achieves notable speedups in comparison to existing approaches for robust subspace recovery.



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