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Reconstruction of a Generalized Joint Sparsity Model using Principal Component Analysis
2011
2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
In this paper, we define a new Joint Sparsity Model (JSM) and use Principal Component Analysis followed by Minimum Description Length and Compressive Sensing to reconstruct spatially and temporally correlated signals in a sensor network. The proposed model decomposes each sparse signal into two sparse components. The first component has a common support across all sensed signals. The second component is an innovation part that is specific to each sensor and might have a support that is
doi:10.1109/camsap.2011.6136001
dblp:conf/camsap/MakhzaniV11
fatcat:vg6rf722xjgcxahqacf6jbwns4