Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics
Abstract
Identifying graph topologies as well as processes evolving over graphs emerge in various applications involving gene-regulatory, brain, power, and social networks, to name a few. Key graph-aware learning tasks include regression, classification, subspace clustering, anomaly identification, interpolation, extrapolation, and dimensionality reduction. Scalable approaches to deal with such high-dimensional tasks experience a paradigm shift to...
Paper Details
Title
Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics
Published Date
May 1, 2018
Journal
Volume
106
Issue
5
Pages
787 - 807
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