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We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection, in a wireless sensor network (WSN). Occurrence of the event of interest is modeled as a binary Gaussian hypothesis test. In this case sensor selection consists of finding, among all (pn) combinations, the one maximizing the Kullback-Leibler (KL) distance between the induced p-dimensional distributions...
This paper addresses robust linear dimensionality reduction (RLDR) for binary Gaussian hypothesis testing. The goal is to find a linear map from the high dimensional space where the data vector lives to a low dimensional space where the hypothesis test is carried out. The linear map is designed to maximize the detector performance. This translates into maximizing the Kullback-Leibler (KL) distance...
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