Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Abstract
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.
Keywords
Cite
@article{arxiv.1009.2009,
title = {Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data},
author = {Tran The Truyen and Dinh Q. Phung and Hung H. Bui and Svetha Venkatesh},
journal= {arXiv preprint arXiv:1009.2009},
year = {2010}
}
Comments
56 pages, short version presented at NIPS'08