Li, Yen-HuanCevher, Volkan2016-02-012016-02-012016-02-01201610.1109/ICASSP.2016.7472435https://infoscience.epfl.ch/handle/20.500.14299/123174WOS:000388373404036The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and solving a non-smooth convex minimization problem (e.g., basis pursuit). In this paper, another approach is considered: We learn a good sub-sampling pattern based on available training signals, without knowing the signal structure in advance, and reconstruct an accordingly sub-sampled signal by computationally much cheaper linear reconstruction. We provide a theoretical guarantee on the recovery error, and show via experiments on real-world MRI data the effectiveness of the proposed compressive MRI scheme.Compressive samplingMagnetic resonance imaging (MRI)LearningLeast squares estimationSub-modular minimizationtext::conference output::conference proceedings::conference paper