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We consider consensus algorithms in their most general setting and provide conditions under which such algorithms are guaranteed to converge, almost surely, to a consensus. Let {A(t),B(t)} isin RNtimesN be (possibly) random, non-stationary matrices and {x(t),m(t)} isin RNtimes1 be state and perturbation vectors, respectively. For any consensus algorithm of the form x(t + 1) = A(t)x(t) + B(t)m(t),...
This paper introduces the generalized Cauchy distribution derived LLp metric. We analyze the properties of the metric from the point of view of robust statistics and relate the metric to the Lp metric, comparing the robustness of the metrics according to their influence functions. The derived metric is employed in robust clustering. To implement the proposed robust clustering method, a robust centroid...
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