Multi-task compressive sensing with Dirichlet process priors

Yuting Qi, Dehong Liu, David Dunson, Lawrence Carin
2008 Proceedings of the 25th international conference on Machine learning - ICML '08  
Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a given signal. In many applications one is interested in multiple signals that may be measured in multiple CS-type measurements, where here each signal corresponds to a sensing "task". In this paper we propose a novel multitask compressive sensing framework based on a Bayesian formalism, where a Dirichlet process (DP) prior is employed, yielding a
more » ... incipled means of simultaneously inferring the appropriate sharing mechanisms as well as CS inversion for each task. A variational Bayesian (VB) inference algorithm is employed to estimate the full posterior on the model parameters.
doi:10.1145/1390156.1390253 dblp:conf/icml/QiLDC08 fatcat:tv4r34wva5ghjlojo5gpgzpbxm