PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones
Katayoun Farrahi and Daniel Gatica-Perez
In: in Proc. Int. Conf. on Multimodal Interfaces (ICMI-MLMI), 02-06 Nov 2009, Cambridge, USA.

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 predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Multimodal Integration
ID Code:6853
Deposited By:Daniel Gatica-Perez
Deposited On:08 April 2010