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dc.contributor.authorPentland, Alex Paulen_US
dc.contributor.authorEagle, Nathan N.en_US
dc.date.accessioned2009-10-19T13:22:45Z
dc.date.available2009-10-19T13:22:45Z
dc.date.issued2009-04en_US
dc.date.submitted2007-09en_US
dc.identifier.issn0340-5443en_US
dc.identifier.issn1432-0762en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/49446
dc.description.abstractLongitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00265-009-0739-0en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.rights.urihttp://www.springerlink.com/help/disclaimer.mpxen_US
dc.sourceNathan Eagleen_US
dc.titleEigenbehaviors: Identifying Structure in Routineen_US
dc.typeArticleen_US
dc.identifier.citationN. Eagle and A. Pentland, “Eigenbehaviors: identifying structure in routine,” Behavioral Ecology and Sociobiology, vol. 63, May. 2009, pp. 1057-1066.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.approverEagle, Nathan N.en_US
dc.contributor.mitauthorPentland, Alex Paulen_US
dc.contributor.mitauthorEagle, Nathan N.en_US
dc.relation.journalBehavioral Ecology and Sociobiologyen_US
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/SubmittedJournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsEagle, Nathan N.; Pentland, Alex Sandyen
dc.identifier.orcidhttps://orcid.org/0000-0002-8053-9983
mit.licensePUBLISHER_POLICYen_US


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