Hierarchical semi-Markov conditional random fields for recursive sequential data

Tran The Truyen, Dinh Q. Phung, Hung H. Bui, Svetha Venkatesh

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

4 Citations (Scopus)


Inspired by the hierarchical hiddenMarkov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov chains to model complex hierarchical, nested Markov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we develop efficient algorithms for learning and constrained inference in a partially-supervised setting, which is important issue in practice where labels can only be obtained sparsely. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Number of pages8
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventAdvances in Neural Information Processing Systems 2008 - Vancouver, Canada
Duration: 8 Dec 200811 Dec 2008
Conference number: 21st
https://dl.acm.org/doi/proceedings/10.5555/2981780 (Proceedings)

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference


ConferenceAdvances in Neural Information Processing Systems 2008
Abbreviated titleNIPS 2008
Internet address

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