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The tasks of online data reduction and outlier rejection are both of high interest when large amounts of data are to be processed for inference. Rather than performing these tasks separately, we propose a joint approach, i.e., robust censoring. We formulate the problem as a non-convex optimization problem based on the data model for outlier-free data, without requiring prior model assumptions about...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
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