JIN , Danqi

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. Similarly to most adaptive filtering algorithms and sparsity-inducing regularization techniques, ZA-LMS appears to face a trade-off between convergence speed and steady-state performance, and between sparsity level and estimation...

Modeling relations between local optimum parameter vectors to estimate in multitask networks has attracted much attention over the last years. This work considers a distributed optimization problem with jointly sparse structure among nodes, that is, the local solutions have the same sparse support set. Several mixed norm have been proposed to...

Modeling relations between local optimum parameter vectors in multitask networks has attracted much attention over the last years. This work considers a distributed optimization problem for parameter vectors with a jointly sparse structure among nodes, that is, the parameter vectors share the same support set. By introducing an L∞,1-norm...

Group zero-attracting LMS (GZA-LMS) and its reweighted variant (GRZA-LMS) have been proposed for system identification with structural group sparsity of the parameter vector. Similar to most adaptive filtering algorithms with regularized penalty, GZA-LMS/GRZA-LMS suffers from a trade-off between convergence rate and steady-state performance,...

Combining diffusion strategies with complementary properties enables enhanced performance when they can be run simultaneously. In this paper, we propose two convex combination schemes, the power-normalized one and the sign-regressor one. Without loss of generality, theoretical investigations are focused on the former. An analysis of...

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and...