Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data

Details Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data

2010-09-10

arxiv.org/abs/1009.2009v1

Statistics

Machine Learning

56 pages, short version presented at NIPS'08

Scientific paper

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we consider partiallysupervised learning and propose algorithms for generalised partially-supervised learning and constrained inference. 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.

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