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• W3007787049 abstract "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." @default.
• W3007787049 created "2020-03-06" @default.
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• W3007787049 date "2020-07-01" @default.
• W3007787049 modified "2023-10-13" @default.
• W3007787049 title "Multitask diffusion affine projection sign algorithm and its sparse variant for distributed estimation" @default.
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• W3007787049 doi "https://doi.org/10.1016/j.sigpro.2020.107561" @default.
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