no code implementations • 21 Mar 2023 • Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.
no code implementations • 6 Mar 2021 • Yilin Chen, Jennifer DeJong, Tom Halverson, David I Shuman
Ranked data sets, where m judges/voters specify a preference ranking of n objects/candidates, are increasingly prevalent in contexts such as political elections, computer vision, recommender systems, and bioinformatics.
no code implementations • 19 Jun 2020 • David I Shuman
Representing data residing on a graph as a linear combination of building block signals can enable efficient and insightful visual or statistical analysis of the data, and such representations prove useful as regularizers in signal processing and machine learning tasks.
1 code implementation • 5 Jan 2014 • Dorina Thanou, David I Shuman, Pascal Frossard
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary.
1 code implementation • 31 Oct 2012 • David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst
In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs.