Hierarchical semi-markov conditional random fields for recursive sequential data
Truyen, Tran The, Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2008, Hierarchical semi-markov conditional random fields for recursive sequential data, in NIPS 2008 : Advances in Neural Information Processing Systems 21 : Proceedings of the 2008 Conference, Curran Associates, Red Hook, N. Y., pp. 1657-1664.
Inspired by the hierarchical hidden Markov 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.
ISBN
9781605609492 1605609498
Language
eng
Field of Research
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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