The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.

Entropy has recently gained considerable significance as an important metric for network measurement. Previous research has shown its utility in clustering traffic and detecting traffic anomalies. While measuring the entropy of the traffic observed at a single point has already been studied, an interesting open problem is to measure the entropy of the traffic between every origin-destination pair. In this paper, we propose the first solution to this challenging problem. Our sketch builds upon and extends the Lp sketch of Indyk with significant additional innovations. We present calculations showing that our data streaming algorithm is feasible for high link speeds using commodity CPU/memory at a reasonable cost. Our algorithm is shown to be very accurate in practice via simulations, using traffic traces collected at a tier-1 ISP backbone link.

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