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Learning multifaceted latent activities from heterogeneous mobile data
conference contribution
posted on 2016-12-22, 00:00 authored by Thanh Binh Nguyen, Tien Vu Nguyen, T Nguyen, Svetha VenkateshSvetha Venkatesh, M Kumar, Quoc-Dinh PhungInferring 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.
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Event
Data Science and Advanced Analytics. IEEE International Conference (3rd : Montreal, Canada)Pagination
389 - 398Publisher
IEEELocation
Montreal, CanadaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2016-10-17End date
2016-10-19ISBN-13
9781509052066Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, IEEETitle of proceedings
DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced AnalyticsUsage metrics
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