Sparse Sampling for Inverse Problems with Tensors
Guillermo Ortiz-Jimenez (Student TU Delft, École Polytechnique Fédérale de Lausanne)
Mario Coutino (TU Delft - Signal Processing Systems)
Sundeep Prabhakar Chepuri (Indian Institute of Science, TU Delft - Signal Processing Systems)
Geert Leus (TU Delft - Signal Processing Systems)
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Abstract
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 multiantenna communications to graph signal processing, to validate the developed theory.