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Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes

Nguyen, Thanh- Bing, Nguyen, Vu, Venkatesh, Svetha and Phung, Dinh 2015, Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes. In Cao, Huiping, Li, Jinyan and Wang, Ruili (ed), Trends and applications in knowledge discovery and data mining, Springer, Berlin, Germany, pp.128-140, doi: 10.1007/978-3-319-42996-0_11.

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Title Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes
Author(s) Nguyen, Thanh- BingORCID iD for Nguyen, Thanh- Bing orcid.org/0000-0003-4527-8826
Nguyen, Vu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Title of book Trends and applications in knowledge discovery and data mining
Editor(s) Cao, Huiping
Li, Jinyan
Wang, Ruili
Publication date 2015
Series Lecture Notes in Computer Science
Chapter number 11
Total chapters 23
Start page 128
End page 140
Total pages 12
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) artificial intelligence
data mining and knowledge discovery
health informatics
Summary Hierarchical Dirichlet processes (HDP) was originally designed and experimented for a single data channel. In this paper we enhanced its ability to model heterogeneous data using a richer structure for the base measure being a product-space. The enhanced model, called Product Space HDP (PS-HDP), can (1) simultaneously model heterogeneous data from multiple sources in a Bayesian nonparametric framework and (2) discover multilevel latent structures from data to result in different types of topics/latent structures that can be explained jointly. We experimented with the MDC dataset, a large and real-world data collected from mobile phones. Our goal was to discover identity–location– time (a.k.a who-where-when) patterns at different levels (globally for all groups and locally for each group). We provided analysis on the activities and patterns learned from our model, visualized, compared and contrasted with the ground-truth to demonstrate the merit of the proposed framework. We further quantitatively evaluated and reported its performance using standard metrics including F1-score, NMI, RI, and purity. We also compared the performance of the PS-HDP model with those of popular existing clustering methods (including K-Means, NNMF, GMM, DP-Means, and AP). Lastly, we demonstrate the ability of the model in learning activities with missing data, a common problem encountered in pervasive and ubiquitous computing applications.
ISBN 9783319429953
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-42996-0_11
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085932

Document type: Book Chapter
Collection: Centre for Pattern Recognition and Data Analytics
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