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The paper deals with the task of robust nonlinear regression in the presence of outliers. The problem is dealt in the context of reproducing kernel Hilbert spaces (RKHS). In contrast to more classical approaches, a recent trend is to model the outliers as a sparse vector noise component and mobilize tools from the sparsity-aware/compressed sensing theory to impose sparsity on it. In this paper, three...
This technical note presents learning coverage control of mobile sensing agents without a priori statistical information regarding random signal locations in a one-dimensional space. In particular, the proposed algorithm controls the usage probability of each agent in a network while simultaneously satisfying an overall network formation topology. The proposed control algorithm is rather direct, not...
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