most citedBinary Linear Classification and Feature Selection via Generalized Approximate Message Passing

23 citations · 23 across the 1 of their papers we have counts for

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2 papers

cs.IT2014★ 23 cited

Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

Justin Ziniel, Philip Schniter, Per Sederberg

For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (…

cs.IT2011

Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

Justin Ziniel, Philip Schniter

In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection o…