Name
Abstract | ||
|---|---|---|
Modern data processing tasks frequently involve structured data, for example signals defined on the vertex set of a weighted graph. In this paper, we address the problem of inference of mobility patterns from data defined on geographical graphs based on spatially localized events. Specifically, we propose a model-based approach where we build a signal model for each of the expected mobility patterns. We then analyze the characteristics of the signal models by studying their spectral representations using wavelets defined on graphs, which enables us to build efficient classifier in the spectral domain. Experiments on data gathered from photo-taking events in Flickr show that we can efficiently infer mobility patterns using only coarse aggregated information, which is certainly interesting in terms of privacy protection. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638232 | ICASSP |
Keywords | Field | DocType |
signal representation,spatially localized events,mobility patterns,spectral graph wavelets,mobility pattern inference,signals on graphs,wavelet transforms,inference mechanisms,pattern classification,signal model,spectral analysis,geographical graphs,spectral domain,signal classification,specral graph wavelets,graph theory,classification,spectral representations,flickr,phototaking events,testing,vectors,computational modeling | Graph theory,Data mining,Data processing,Pattern recognition,Vertex (geometry),Computer science,Inference,Artificial intelligence,Classifier (linguistics),Data model,Wavelet transform,Wavelet | Conference |
ISSN | Citations | PageRank |
1520-6149 | 4 | 0.42 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowen Dong | 1 | 249 | 22.07 |
| Antonio Ortega | 2 | 4720 | 493.26 |
| Pascal Frossard | 3 | 3015 | 230.41 |
| Pierre Vandergheynst | 4 | 3576 | 208.25 |