Multi-task compressive sensing with Dirichlet process priors

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 principled 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.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Yuting Qi, Dehong Liu, David B. Dunson, Lawrence Carin
• Keywords: Appropriate Sharing Mechanisms, Compressive Sensing Framework, ICML 2008, Machine Learning, Multiple Cs-type Measurements