Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling
 
conference paper

Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling

Farrahi, Katayoun  
•
Gatica-Perez, Daniel  
2010
2010 IEEE Second International Conference on Social Computing
2010 IEEE Second International Conference on Social Computing, SIN Symposium

In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to probabilistic topic models. We use an unsupervised learning approach that discovers human routines of varying durations ranging from half-hourly to several hours. Our methodology can handle large sequence lengths based on a principled procedure to deal with potentially large routine-vocabulary sizes, and can be applied to rather naive initial vocabularies to discover meaningful location-routines. We successfully apply the model to a large, real-life dataset, consisting of 97 cellphone users and 16 months of their location patterns, to discover routines with varying time durations.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

Farrahi_SOCIALCOM-2_2010.pdf

Access type

openaccess

Size

138.42 KB

Format

Adobe PDF

Checksum (MD5)

71c73ed26bf9bda293e1762d7be3f3f2

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés