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Graph Signal Processing: Overview, Challenges and Applications
Antonio Ortega, Pascal Frossard, Jelena Kovačević +2
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in…
Closed Form Solutions of Combinatorial Graph Laplacian Estimation under Acyclic Topology Constraints
Keng-Shih Lu, Antonio Ortega
How to obtain a graph from data samples is an important problem in graph signal processing. One way to formulate this graph learning problem is based on Gaussian maximum likelihood…
Learning Graphs with Monotone Topology Properties and Multiple Connected Components
Eduardo Pavez, Hilmi E. Egilmez, Antonio Ortega
Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation with graph Laplacian constraints. While such problems are convex, existin…
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
Aamir Anis, Aly El Gamal, Salman Avestimehr +1
Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the data…