Graph Learning Under Spectral Sparsity Constraints (original) (raw)

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Pierre Borgnat

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Kernel-Based Reconstruction of Graph Signals

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Graph-based Learning under Perturbations via Total Least-Squares

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Network topology identification from spectral templates

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Discriminating graphs through spectral projections

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From graphs to signals and back: Identification of network structures using spectral analysis

Patrick Flandrin

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LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees

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arXiv (Cornell University), 2023

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Sparse Quadratic Approximation for Graph Learning

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Graph Kernels by Spectral Transforms

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Vertex-Frequency Analysis: A Way to Localize Graph Spectral Components [Lecture Notes]

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A spectral approach to learning structural variations in graphs

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Pattern Recognition, 2006

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Statistical Graph Signal Recovery Using Variational Bayes

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arXiv (Cornell University), 2020

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Vertex-Frequency Graph Signal Processing

Anthony Constantinides

ArXiv, 2019

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On Local Distributions in Graph Signal Processing

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ArXiv, 2022

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Complex Basis For Spectral Analysis of Graph Signals

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A Simple Baseline Algorithm for Graph Classification

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Robust Least Mean Squares Estimation of Graph Signals

Sergiy A . Vorobyov

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Dynamic Graph Learning Based on Graph Laplacian

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Graph Similarity based on Graph Fourier Distances

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