File(s) under permanent embargo
Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes
conference contribution
posted on 2013-01-01, 00:00 authored by Cong Thuong Nguyen, Quoc-Dinh Phung, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha VenkateshA 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.
History
Event
Pervasive Computing and Commmunications. IEEE International Conference (11th : 2013 : San Diego, California)Pagination
47 - 55Publisher
IEEELocation
San Diego, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-03-18End date
2013-03-22ISBN-13
9781467345750ISBN-10
146734575XLanguage
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
PerCom 2013 : Proceedings of the 11th IEEE International Conference on Pervasive Computing and CommmunicationsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC