Multitask diffusion affine projection sign algorithm and its sparse variant for distributed estimation

Jingen Ni, Yanan Zhu, Jie Chen

科研成果: 期刊稿件文章同行评审

24 引用 (Scopus)

摘要

Distributed adaptation over multitask networks has attracted particular attention due to its enhanced modeling capacity compared to that over conventional single-task networks. Most of the existing works derive their multitask adaptive algorithms using mean-square error (MSE) or least squares (LS) criterion, leading to multitask LMS- or LS-type algorithms. These algorithms, however, may suffer from deteriorated convergence rate or even divergence in impulsive noise environments. In order to address this problem, we propose a robust diffusion affine projection sign algorithm for multitask parameter estimation. The algorithm is derived by using the method of data reusing and minimizing the weighted sum of the l1-norms of some intermediate error vectors plus a similarity term subject to constraints on intermediate weight vectors at each agent. The multitask similarity relationship is characterized by the distance regularization among weight vectors. Furthermore, a variant of this algorithm, which is obtained by further regularizing the cost function by the l0-norm of the intermediate weight vector at each agent, is presented to promote convergence rate for jointly sparse parameter vectors estimation.

源语言英语
文章编号107561
期刊Signal Processing
172
DOI
出版状态已出版 - 7月 2020

指纹

探究 'Multitask diffusion affine projection sign algorithm and its sparse variant for distributed estimation' 的科研主题。它们共同构成独一无二的指纹。

引用此