Modeling group dynamics using graphical models and tensor decompositions

Lin Li, Ananthram Swami, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a general modeling framework for learning group dynamics in data collected from multiple information sources and over time. In particular, groups are characterized by specific temporal patterns based on hidden Markov models. Tensor decomposition techniques combined with graphical models are used to extract group information from the observed data.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages793-797
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Externally publishedYes
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems

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  • Cite this

    Li, L., Swami, A., & Scaglione, A. (2014). Modeling group dynamics using graphical models and tensor decompositions. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 793-797). [7032228] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032228