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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 thedoi:10.1145/1647314.1647373 dblp:conf/icmi/FarrahiG09 fatcat:k4w3rtcezjhhxn3upie6mxti6u