Proportionate Adaptive Graph Signal Recovery
نویسندگان
چکیده
This paper generalizes the proportionate-type adaptive algorithm to graph signal processing and proposes two recovery algorithms. The gain matrix of proportionate leads faster convergence than least mean squares (LMS) algorithm. In this paper, is obtained in a closed-form by minimizing gradient mean-square deviation (GMSD). first LMS (Pt-GLMS) which simply uses recursion process accelerates Pt-GLMS compared second extended (Pt-GELMS) algorithm, previous vectors alongside current iteration. Pt-GELMS utilizes matrices control effect iterations. stability analyses algorithms are also provided. Simulation results demonstrate efficacy proposed
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal and Information Processing over Networks
سال: 2023
ISSN: ['2373-776X', '2373-7778']
DOI: https://doi.org/10.1109/tsipn.2023.3277591