Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

26 Jun 2018Shanshan WuAlexandros G. DimakisSujay SanghaviFelix X. YuDaniel Holtmann-RiceDmitry StorcheusAfshin RostamizadehSanjiv Kumar

Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we present a new method to learn linear encoders that adapt to data, while still performing well with the widely used $\ell_1$ decoder... (read more)

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