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Found 5 papers, 0 papers with code
Lossy Compression of Adjacency Matrices by Graph Filter Banks
In this paper, we propose a lossy compression of weighted adjacency matrices, where the binary adjacency information is encoded losslessly (so the topological information of the graph is preserved) while the edge weights are compressed lossily.
Optimizing $k$ in $k$NN Graphs with Graph Learning Perspective
$k$NN is one of the most popular approaches and is widely used in machine learning and signal processing.
Dynamic Sensor Placement Based on Graph Sampling Theory
Sensor placement problem aims to select K sensor positions from N candidates where K < N. Most existing methods assume that sensor positions are static, i. e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time.
Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks
In this paper, we consider multi-channel sampling (MCS) for graph signals.
Graph Signal Sampling Under Stochastic Priors
We propose a generalized sampling framework for stochastic graph signals.
