Distributed sparse canonical correlation analysis in clustering sensor data

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 algorithm is also put forth for informative sensor identification in nonlinear settings using the novel CCA approach. Toward this end, the sparsity-aware CCA framework is reformulated as a separable constrained minimization problem which is solved by utilizing block coordinate descent techniques combined with the alternating direction method of multipliers. Numerical tests demonstrate that the distributed sparse CCA scheme put forth outperforms existing alternatives when it comes to clustering the sensor data based on their source content.