logo Idiap Research Institute        
 [BibTeX] [Marc21]
Learning and Predicting Multimodal Daily Life Patterns from Cell Phones
Type of publication: Conference paper
Citation: Farrahi_ICMI-MLMI_2009
Booktitle: ICMI-MLMI
Year: 2009
Abstract: In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the unsupervised learning of topics describing people's most common locations jointly with the most common types of interactions at these locations. We further successfully predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.
Projects Idiap
Authors Farrahi, Katayoun
Gatica-Perez, Daniel
Added by: [UNK]
Total mark: 0
  • Farrahi_ICMI-MLMI_2009.pdf