  This paper proposes a robust adaptive algorithm for smooth graph signal
recovery which is based on generalized correntropy. A proper cost function is
defined for this purpose. The proposed algorithm is derived and a kernel width
learning-based version of the algorithm is suggested which the simulation
results show the superiority of it to the fixed correntropy kernel version of
the algorithm. Moreover, some theoretical analysis of the proposed algorithm
are provided. In this regard, firstly, the convexity analysis of the cost
function is discussed. Secondly, the uniform stability of the algorithm is
investigated. Thirdly, the mean convergence analysis is also added. Finally,
the complexity analysis of the algorithm is incorporated. In addition, some
synthetic and real-world experiments show the advantage of the proposed
algorithm in comparison to some other adaptive algorithms in the literature of
adaptive graph signal recovery.
