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Orthogonal Transforms for Signals on Directed Graphs
Julia Barrufet, Antonio Ortega
In this paper we consider the problem of defining transforms for signals on directed graphs, with a specific focus on defective graphs where the corresponding graph operator cannot…
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…
Graph Signal Processing over a Probability Space of Shift Operators
Feng Ji, Wee Peng Tay, Antonio Ortega
Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. Howeve…
Multi-resolution intra-predictive coding of 3D point cloud attributes
Eduardo Pavez, Andre L. Souto, Ricardo L. De Queiroz +1
We propose an intra frame predictive strategy for compression of 3D point cloud attributes. Our approach is integrated with the region adaptive graph Fourier transform (RAGFT), a m…
DCT and DST Filtering with Sparse Graph Operators
Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee +1
Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the…
Practical graph signal sampling with log-linear size scaling
Ajinkya Jayawant, Antonio Ortega
Graph signal sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices.…