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In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by introducing the historical data and the sparsity feature based on compressive sensing. A reconstruction algorithm based on the alternating direction method...
Variable selection for high-dimensional data problems, including both regression and classification, has been a subject of intense research activities in recent years. Many promising solutions have been proposed. However, less attention has been given to the case when some of the data are missing. This paper proposes a general approach to high-dimensional variable selection with the presence of missing...
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