Dictionary based reconstruction and classification of randomly sampled sensor network data


In this paper, we propose a method for recovering and classifying WSN data while minimizing the number of samples that need to be acquired, processed, and transmitted. The problem is formulated according to the recently proposed framework of Matrix Completion (MC), which asserts that a low rank matrix can be recovered from a small number of randomly sampled… (More)
DOI: 10.1109/SAM.2012.6250443


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