000184977 001__ 184977
000184977 005__ 20190316235613.0
000184977 02470 $$2ISI$$a000329611503056
000184977 037__ $$aCONF
000184977 245__ $$aInference of Mobility Patterns via Spectral Graph Wavelets
000184977 269__ $$a2013
000184977 260__ $$c2013
000184977 336__ $$aConference Papers
000184977 520__ $$aModern 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.
000184977 6531_ $$asignals on graphs
000184977 6531_ $$aspectral graph wavelets
000184977 6531_ $$amobility patterns
000184977 6531_ $$aclassification
000184977 6531_ $$aFlickr
000184977 700__ $$0242933$$g193962$$aDong, Xiaowen
000184977 700__ $$aOrtega, Antonio
000184977 700__ $$0241061$$g101475$$aFrossard, Pascal
000184977 700__ $$0240428$$g120906$$aVandergheynst, Pierre
000184977 7112_ $$dMay 26-31, 2013$$cVancouver, Canada$$aIEEE ICASSP
000184977 773__ $$tProceedings of ICASSP
000184977 8564_ $$uhttps://infoscience.epfl.ch/record/184977/files/icassp_flickr_final.pdf$$zn/a$$s4327949$$yn/a
000184977 909C0 $$xU10380$$0252392$$pLTS2
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000184977 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:184977
000184977 917Z8 $$x193962
000184977 917Z8 $$x193962
000184977 917Z8 $$x101475
000184977 937__ $$aEPFL-CONF-184977
000184977 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000184977 980__ $$aCONF