Papers › Sparse Sampling for Inverse Problems with Tensors
Sparse Sampling for Inverse Problems with Tensors
Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus
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We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop low-complexity greedy algorithms based on submodular optimization methods to compute near-optimal sampling sets. We present several numerical examples, ranging from multi-antenna communications to graph signal processing, to validate the developed theory.
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