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In this paper, we present a method for jointly learning r > 1 similar classification tasks. We consider a set of classification tasks whose relevant features may have some overlap. This potential overlap encourages the idea of learning tasks simultaneously. Our method is based on the idea of using two regularizers which control the underlying structure of the model from completely unrelated tasks...
Motivated by sensor network applications, in this paper we study the problem of a decentralized network of J sensors, in which each sensor observes either all or some components of an underlying sparse signal ensemble. Sensors operate with no collaboration with each other or the fusion center. Each sensor transmits a subset of its linear measurements to the fusion center. The fusion center gathers...
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