Learning multifaceted latent activities from heterogeneous mobile data

Thanh-Binh Nguyen, Vu Nguyen, Thuong Nguyen, Svetha Venkatesh, Mohan Kumar, Dinh Phung

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

1 Citation (Scopus)


Inferring abstract contexts and activities from heterogeneous data is vital to context-Aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset-The StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
Subtitle of host publication17-19 October 2016 Montreal, PQ, Canada
EditorsRavi Kumar, Evangelos Milios
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781509052066
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Data Science and Advanced Analytics 2016 - Montreal, Canada
Duration: 17 Oct 201619 Oct 2016
Conference number: 3rd
https://ieeexplore.ieee.org/xpl/conhome/7795280/proceeding (Proceedings)


ConferenceIEEE International Conference on Data Science and Advanced Analytics 2016
Abbreviated titleDSAA 2016
Internet address


  • Activity recognition
  • Bayesian nonparametrics
  • Context-Aware computing
  • Hierarchical Dirichlet processes
  • Mobile data analytics
  • Product-space

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