Learning conditional latent structures from multiple data sources

Viet Huynh, Dinh Phung, Long Nguyen, Svetha Venkatesh, Hung H. Bui

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Data usually present in heterogeneous sources. When dealing with multiple data sources, existing models often treat them independently and thus can not explicitly model the correlation structures among data sources. To address this problem, we propose a full Bayesian nonparametric approach to model correlation structures among multiple and heterogeneous datasets. The proposed framework, first, induces mixture distribution over primary data source using hierarchical Dirichlet processes (HDP). Once conditioned on each atom (group) discovered in previous step, context data sources are mutually independent and each is generated from hierarchical Dirichlet processes. In each specific application, which covariates constitute content or context(s) is determined by the nature of data. We also derive the efficient inference and exploit the conditional independence structure to propose (conditional) parallel Gibbs sampling scheme. We demonstrate our model to address the problem of latent activities discovery in pervasive computing using mobile data. We show the advantage of utilizing multiple data sources in terms of exploratory analysis as well as quantitative clustering performance.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19–22, 2015 Proceedings, Part 1
EditorsTru Cao , Ee-Peng Lim, Zhi-Hua Zhou , Tu-Bao Ho, David Cheung , Hiroshi Motoda
Place of PublicationCham Switzerland
Number of pages12
ISBN (Print)9783319180373
Publication statusPublished - 2015
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2015 - Ho Chi Minh City, Vietnam
Duration: 19 May 201522 May 2015
Conference number: 19th
https://link.springer.com/book/10.1007/978-3-319-18038-0 (Proceedings)

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2015
Abbreviated titlePAKDD 2015
CityHo Chi Minh City
Internet address

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