Sublinear time, approximate model-based sparse recovery for all

• Publication date: 2011

We describe a probabilistic, {\it sublinear} runtime, measurement-optimal system for model-based sparse recovery problems through dimensionality reducing, {\em dense} random matrices. Specifically, we obtain a linear sketch $u\in \R^M$ of a vector $\bestsignal\in \R^N$ in high-dimensions through a matrix $\Phi \in \R^{M\times N}$ $(M

Reference

• EPFL-CONF-174715

Record created on 2012-02-02, modified on 2012-03-21