TY - CPAPER
AB - 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.
T1 - Inference of Mobility Patterns via Spectral Graph Wavelets
DA - 2013
AU - Dong, Xiaowen
AU - Ortega, Antonio
AU - Frossard, Pascal
AU - Vandergheynst, Pierre
JF - Proceedings of ICASSP
ID - 184977
KW - signals on graphs
KW - spectral graph wavelets
KW - mobility patterns
KW - classification
KW - Flickr
UR - http://infoscience.epfl.ch/record/184977/files/icassp_flickr_final.pdf
ER -