Bayesian Compressive Sensing and Pro jection Optimization

Bayesian Compressive Sensing and Pro jection Optimization
Shihao Ji - Duke University, USA Lawrence Carin - Duke University, USA
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machinelearning community.

Shihao Ji - Duke University, USA
Lawrence Carin - Duke University, USA

This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machinelearning community.