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Learning multifaceted latent activities from heterogeneous mobile data

Nguyen, Thanh-Binh, Nguyen, Tien Vu, Nguyen, Thanh Binh, Venkatesh, Svetha, Kumar, Mohan and Phung, Quoc-Dinh 2016, Learning multifaceted latent activities from heterogeneous mobile data, in DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 389-398, doi: 10.1109/DSAA.2016.48.

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Title Learning multifaceted latent activities from heterogeneous mobile data
Author(s) Nguyen, Thanh-BinhORCID iD for Nguyen, Thanh-Binh
Nguyen, Tien Vu
Nguyen, Thanh BinhORCID iD for Nguyen, Thanh Binh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Kumar, MohanORCID iD for Kumar, Mohan
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh
Conference name Data Science and Advanced Analytics. IEEE International Conference (3rd : Montreal, Canada)
Conference location Montreal, Canada
Conference dates 17-19 Oct. 2016
Title of proceedings DSAA 2016 : Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics
Publication date 2016
Conference series Data Science and Advanced Analytics IEEE International Conference
Start page 389
End page 398
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) activity recognition
Bayesian nonparametrics
hierarchical Dirichlet processes
data analytics,
context-aware computing
Summary 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.
ISBN 9781509052066
Language eng
DOI 10.1109/DSAA.2016.48
Field of Research 080699 Information Systems not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
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Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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