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. Attached Files Name Description MIMEType Size Downloads Title Learning multi-faceted activities from heterogeneous data with the product space hierarchical Dirichlet processes Author(s) Nguyen, Thanh- Bing orcid.org/0000-0003-4527-8826 Nguyen, Vu Venkatesh, Svetha 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 06 Sep 2016, 12:52:20 EST Wed, 07 Sep 2016, 05:02:22 EST Wed, 07 Sep 2016, 06:11:12 EST Wed, 19 Oct 2016, 08:43:39 EST Tue, 08 Nov 2016, 14:13:10 EST Fri, 02 Dec 2016, 15:32:05 EST Tue, 06 Dec 2016, 09:59:50 EST Wed, 11 Jan 2017, 09:47:39 EST Wed, 12 Apr 2017, 11:00:44 EST Mon, 01 May 2017, 09:25:36 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 90 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 06 Sep 2016, 12:52:20 EST

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