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A novel communication efficient scheme for field reconstruction is put forth. The proposed framework entails two steps. During the first step sparsity-inducing canonical correlation is utilized to determine different clusters of correlated sensors. The second step relies on least mean-squares adaptive filters to learn, at a cluster head sensor, the data model of all other cluster sensors. The cluster...
The task of determining informative sensors and clustering the sensor measurements according to their information content is considered. To this end, the standard canonical correlation analysis (CCA) framework is equipped with norm-one and norm-two regularization terms to estimate the unknown number of field sources and identify informative groups of sensors. Coordinate descent techniques are combined...
A regularized canonical correlations scheme is proposed for adaptive clustering of sensor measurements according to their information content. A novel framework utilizing sparsity-inducing regularization and exponential weighing is designed to deal with nonstationary settings. Distributed recursions to minimize the proposed formulation are put forth by utilizing coordinate descent techniques combined...
The problem of determining information-bearing sensors in the presence of multiple field sources and (non-)linear data models is considered. To this end, a novel canonical correlation analysis (CCA) framework combined with norm-one regularization is introduced to identify correlated measurements across the distributed sensors and cluster the sensor data based on their source content. A distributed...
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