Distributed Regression in Sensor Networks With a Reduced-Order Kernel Model
Over the past few years, wireless sensor networks received tremendous attention for monitoring physical phenomena, such as the temperature field in a given region. Applying conventional kernel regression methods for functional learning such as support vector machines is inappropriate for sensor networks, since the order of the resulting model and its computational complexity scales badly with the number of available sensors, which tends to be large. In order to circumvent this drawback, the authors propose in this paper a reduced-order model approach. To this end, they take advantage of recent developments in sparse representation literature, and show the natural link between reducing the model order and the topology of the deployed sensors.