Presentation 2014-03-13
Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA
Yoshitatsu MATSUDA, Kazunori YAMAGUCHI, Ken-ichiro NISHIOKA,
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Abstract(in English) In this paper, we extract various patterns of the spatio-temporal distribution from Foursquare data. For extracting patterns, we employ ICA (Independent Component Analysis), which is a widely-used method in signal processing and feature extraction. Because the Foursquare data consists of check-in's at some time points and locations, ICA is not directly applicable to the data. In order to smooth the data, we estimate a continuous spatio-temporal distribution by employing a diffusion-type formula. The experiments on the actual Foursquare dataset showed that the proposed method could extract some plausible and interesting spatio-temporal patterns. They also suggested that the estimation with the proposed method is promising when the sample size is limited.
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Keyword(in English) spatio-temporal pattern / diffusion-type estimation / independent component analysis
Paper # PRMU2013-173
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Committee PRMU
Conference Date 2014/3/6(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Discovery of Spatio-Temporal Patterns from Foursquare by Diffusion-type Estimation and ICA
Sub Title (in English)
Keyword(1) spatio-temporal pattern
Keyword(2) diffusion-type estimation
Keyword(3) independent component analysis
1st Author's Name Yoshitatsu MATSUDA
1st Author's Affiliation Department of General Systems Studies, The University of Tokyo()
2nd Author's Name Kazunori YAMAGUCHI
2nd Author's Affiliation Department of General Systems Studies, The University of Tokyo
3rd Author's Name Ken-ichiro NISHIOKA
3rd Author's Affiliation Department of General Systems Studies, The University of Tokyo
Date 2014-03-13
Paper # PRMU2013-173
Volume (vol) vol.113
Number (no) 493
Page pp.pp.-
#Pages 6
Date of Issue