6 papers · 1 filter
Graph-Based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-Irregular Sensor Data
Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properti…
Fast DCT+: A Family of Fast Transforms Based on Rank-One Updates of the Path Graph
Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega
This paper develops fast graph Fourier transform (GFT) algorithms with O(n log n) runtime complexity for rank-one updates of the path graph. We first show that several commonly-use…
Irregularity-Aware Bandlimited Approximation for Graph Signal Interpolation
Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
In most work to date, graph signal sampling and reconstruction algorithms are intrinsically tied to graph properties, assuming bandlimitedness and optimal sampling set choices. How…
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields
Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez +1
This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-call…
Spectral folding and two-channel filter-banks on arbitrary graphs
Eduardo Pavez, Benjamin Girault, Antonio Ortega +1
In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time do…
An efficient algorithm for graph Laplacian optimization based on effective resistances
Eduardo Pavez, Antonio Ortega
In graph signal processing, data samples are associated to vertices on a graph, while edge weights represent similarities between those samples. We propose a convex optimization pr…