论文标题
强大的自适应广义基于Correntropy的平滑图信号恢复，并具有内核宽度学习
Robust Adaptive Generalized Correntropy-based Smoothed Graph Signal Recovery with a Kernel Width Learning
论文作者
论文摘要
本文提出了一种基于广义correntropy的平滑图信号恢复的强大自适应算法。为此目的定义了适当的成本函数。提出了所提出的算法，并建议基于内核宽度学习的版本，其中仿真结果显示了其与算法的固定correntropy内核版本的优越性。此外，还提供了对拟议算法的一些理论分析。在这方面，首先讨论了成本函数的凸度分析。其次，研究了该算法的均匀稳定性。第三，还添加了平均收敛分析。最后，纳入了算法的复杂性分析。此外，与自适应图信号恢复文献中的其他一些自适应算法相比，一些合成和现实世界实验显示了所提出的算法的优势。
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.
