In this paper, we investigate the transient performance of the proposed distributed multitask learning algorithm that is developed based on maximum correntropy criterion. In the first stage, we derive the proposed multitask learning algorithm in which the correntropy-based combination matrix determines which sensors should collaborate together and which sensors should stop the collaboration. In the second stage, according to the variance relation of the error vector, we derive a closed-form relation that shows the transition of mean-square-deviation (MSD) learning performance. We also find the lower and upper bounds of the step-size that ensure the stability of the multitask learning algorithm. The theoretical finding of the transient performance is shown to fit a well match with simulation results. Copyright⃝c 2017 John Wiley & Sons, Ltd.

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