Add to ORKGWe develop lagged Metropolis-Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. We explain how to apply the technique together with designed graphs for sampling of units-in-space. We illustrate that the proposed graph spatial sampling approach can be more flexible for improving the design efficiency compared to the existing spatial sampling methods
This work merges tools from graph signal processing and linear systems theory to propose sampling st...
A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signal...
© 2015 Elsevier B.V. All rights reserved. Traditional graph sampling methods reduce the size of a la...
Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph ...
We propose a family of lagged random walk sampling methods in simple undirected graphs, where transi...
International audienceIrregularly sampling a spatially stationary random field does not yield a grap...
A novel scheme for sampling graph signals is proposed. Space-shift sampling can be understood as a h...
Estimating characteristics of large graphs via sampling is a vital part of the study of complex netw...
Given a huge real graph, how can we derive a representative sample? There are many known algorithms ...
A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatia...
Randomized graph sampling (RGS) is an approach for sampling populations associated with or describab...
The random sampling on graph signals is one of the fundamental topics in graph signal processing. In...
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and t...
Discrete spatial structures are ubiquitous in statistical analysis. They can take the form of images...
International audienceWe present a new random sampling strategy for k-bandlimited signals defined on...
This work merges tools from graph signal processing and linear systems theory to propose sampling st...
A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signal...
© 2015 Elsevier B.V. All rights reserved. Traditional graph sampling methods reduce the size of a la...
Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph ...
We propose a family of lagged random walk sampling methods in simple undirected graphs, where transi...
International audienceIrregularly sampling a spatially stationary random field does not yield a grap...
A novel scheme for sampling graph signals is proposed. Space-shift sampling can be understood as a h...
Estimating characteristics of large graphs via sampling is a vital part of the study of complex netw...
Given a huge real graph, how can we derive a representative sample? There are many known algorithms ...
A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatia...
Randomized graph sampling (RGS) is an approach for sampling populations associated with or describab...
The random sampling on graph signals is one of the fundamental topics in graph signal processing. In...
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and t...
Discrete spatial structures are ubiquitous in statistical analysis. They can take the form of images...
International audienceWe present a new random sampling strategy for k-bandlimited signals defined on...
This work merges tools from graph signal processing and linear systems theory to propose sampling st...
A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signal...
© 2015 Elsevier B.V. All rights reserved. Traditional graph sampling methods reduce the size of a la...