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Ali H. Sayed
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 766
Citations - 39568
Ali H. Sayed is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Adaptive filter & Optimization problem. The author has an hindex of 81, co-authored 728 publications receiving 36030 citations. Previous affiliations of Ali H. Sayed include Harbin Engineering University & University of California, Los Angeles.
Papers
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Proceedings ArticleDOI
Clustering via diffusion adaptation over networks
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: This work devise an adaptive combination rule that allows agents to learn which neighbors belong to the same cluster and which other neighbors should be ignored, and enables the agents to identify their grouping and to attain improved learning and estimation performance over networks.
Proceedings ArticleDOI
Diffusion adaptive networks with changing topologies
Cassio G. Lopes,Ali H. Sayed +1 more
TL;DR: This work extends prior work to changing topologies and data-normalized algorithms and develops a probabilistic diffusion adaptive network, a simpler yet robust variant of the standard diffusion algorithm.
Journal ArticleDOI
Asynchronous Adaptation and Learning Over Networks—Part I: Modeling and Stability Analysis
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: In this paper, the stability and performance of asynchronous strategies for distributed optimization and adaptation problems over networks were analyzed, and the results provided a solid justification for the remarkable resilience of cooperative networks in the face of random failures at multiple levels: agents, links, data arrivals and topology.
Journal ArticleDOI
Optimal Linear Fusion for Distributed Detection Via Semidefinite Programming
TL;DR: It is shown that the two nonconvex subproblems can be solved by semidefinite programs in a globally optimal fashion and by employing a special matrix decomposition technique, the optimal linear fusion rule for the distributed detection problem is obtained.
Journal ArticleDOI
Steady-State MSE Performance Analysis of Mixture Approaches to Adaptive Filtering
TL;DR: This paper investigates mixture structures with respect to their final mean-square error (MSE) and tracking performance in the steady state for stationary and certain nonstationary data, respectively.