Multitask Compressive Sensing

By Shihao Ji, David B. Dunson, Lawrence Carin. IEEE Transactions on Signal Processing, 2009

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Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, methodology): “in Fig. 12 that spurious peaks can be suppressed by properly setting up parameters of SBI-SVD when ˙2 can be known, where we set c= 1 and d= KM˙2.6 This work is related to recent works on Bayesian CS [30,35,37], in which the CS reconstruction problem is formulated from a Bayesian perspective while s”
Distributed Compressive Sensing: A Deep Learning Approach (2015, methodology): “Although it is based on a greedy method that selects subsets that are not necessarily optimal, we experimentally show that by using a properly trained model and only one layer of LSTM, the proposed method sig-niﬁcantly outperforms well known MMV baselines (e.g., SOMP) as well as the well known Bayes”
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project (2013, background): “Another possibility is to exploit a priori information about the data in a Bayesian fashion (Ji et al., 2009; Bilgic et al., 2011) to further improve the CS reconstruction of single- and multi-shell diffusion data with fewer q-space data points (Duarte-Carvajalino et al.”
Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, methodology): “iteratively reweighted algorithm [118], sparse Bayesian learning algorithm [119], and Kalman-based sparse recovery algorithm [103] has been proposed.”