Distributed Matrix Completion: Application to Cooperative Positioning in Noisy Environments
Final rept. Jul 2010-Dec 2013
STANFORD UNIV CA
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The PI and collaborators developed novel algorithms for positioning, building on earlier developments in matrix completion and high-dimensional statistics. In particular, a distributed version of matrix completion-based positioning, and a gossip version of low-rank approximation were developed. A convex relaxation for positioning in the presence of noise was shown to be constant-optimal. Additional contributions were made in several other areas Finding dense substructures of large networks in nearly linear time Approximate message passing algorithms and in particular their application to spatially-coupled compressed sensing Measures of statistical significance in high dimension.
- Statistics and Probability
- Miscellaneous Detection and Detectors