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Multi-task compressive sensing with Dirichlet process priors
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
doi:10.1145/1390156.1390253
dblp:conf/icml/QiLDC08
fatcat:tv4r34wva5ghjlojo5gpgzpbxm