论文标题
比例自适应图信号恢复
Proportionate Adaptive Graph Signal Recovery
论文作者
论文摘要
本文将比例型自适应算法推广到图形信号处理，并提出了两种比例型自适应图形信号恢复算法。比例算法的增益矩阵导致比最小平方（LMS）算法更快地收敛。在本文中，通过最小化均方偏差（GMSD）的梯度，以封闭形式获得增益矩阵。第一种算法是比例型图LMS（PT-GLMS）算法，该算法仅在LMS算法的递归过程中使用增益矩阵，并加速与LMS算法相比，PT-GLMS算法的收敛性。第二个算法是比例型图扩展LMS（PT-GELMS）算法，该算法使用先前的信号向量与当前迭代的信号旁边。 Pt-gelms算法利用两个增益矩阵来控制先前迭代的信号的效果。还提供了算法的稳定性分析。仿真结果证明了两种拟议的比例型LMS算法的功效。
This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence than least mean squares (LMS) algorithm. In this paper, the gain matrix is obtained in a closed-form by minimizing the gradient of the mean-square deviation (GMSD). The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm. The second algorithm is the Proportionate-type Graph Extended LMS (Pt-GELMS) algorithm, which uses the previous signal vectors alongside the signal of the current iteration. The Pt-GELMS algorithm utilizes two gain matrices to control the effect of the signal of the previous iterations. The stability analyses of the algorithms are also provided. Simulation results demonstrate the efficacy of the two proposed proportionate-type LMS algorithms.
