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Fault Identification Via Nonparametric Belief Propagation

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5 Author(s)
Danny Bickson ; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA ; Dror Baron ; Alexander Ihler ; Harel Avissar
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We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately, maximum a posteriori (MAP) probability estimation of the fault pattern is computationally intractable. To solve the fault identification problem, we propose a nonparametric belief propagation (NBP) approach. We show empirically that our belief propagation solver is more accurate than recent state-of-the-art algorithms including interior point methods and semidefinite programming. Our superior performance is explained by the fact that we take into account both the binary nature of the individual faults and the sparsity of the fault pattern arising from their rarity.

Published in:

IEEE Transactions on Signal Processing  (Volume:59 ,  Issue: 6 )