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Domain adaptation (DA) algorithms address the problem of distribution shift between training and testing data. Recent approaches transform data into a shared subspace by minimizing the shift between their marginal distributions. We propose a method to learn a common subspace that will leverage the class conditional distributions of training samples along with reducing the marginal distribution shift...
This paper deals with the system-level fault diagnosis problem which main objective is to identify faults, in particular permanent ones, in diagnosable systems under the PMC model. The PMC model assumes that each system's node is tested by a subset of the other nodes, and that at most t of these nodes are permanently faulty. Tests performed by faulty nodes are unreliable, and hence, they can incorrectly...
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