Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

Nguyen, Thuong, Phung, Dinh, Gupta, Sunil and Venkatesh, S. 2013, Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes, in PerCom 2013 : Proceedings of the 11th IEEE International Conference on Pervasive Computing and Commmunications, IEEE, Piscataway, N.J., pp. 47-55, doi: 10.1109/PerCom.2013.6526713.

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Title Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes
Author(s) Nguyen, Thuong
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Venkatesh, S.ORCID iD for Venkatesh, S. orcid.org/0000-0001-8675-6631
Conference name Pervasive Computing and Commmunications. IEEE International Conference (11th : 2013 : San Diego, California)
Conference location San Diego, California
Conference dates 18-22 Mar. 2013
Title of proceedings PerCom 2013 : Proceedings of the 11th IEEE International Conference on Pervasive Computing and Commmunications
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Pervasive Computing and Commmunications
Start page 47
End page 55
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Summary A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.
ISBN 146734575X
9781467345750
Language eng
DOI 10.1109/PerCom.2013.6526713
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057188

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