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In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor’s observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference...
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification which exploits correlation as well as complementary information among homogeneous and heterogeneous sensors while simultaneously extracting the low-rank interference term. Specifically, we observe that incorporating the noise or interfered signal as a low-rank component is essential in...
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