Efficient Spatial Classification Using Decoupled Conditional Random Field
- Chi-Hoon Lee, Dept of Computing Science
- Russ Greiner, Dept of Computing Science; PI of AICML
- Osmar R. Zaiane, University of Alberta (Database)
We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accuracy. As CRFs are discriminative, they are typically more accurate than the generative MRFs. This also means their learning process is more expensive. This paper addresses this situation by defining and using “Decoupled Conditional Random Fields (DCRFs)”, a variant of CRFs whose learning process is more efficient as it decouples the tasks of learning potentials. Although our model is only guaranteed to approximate a CRF, our empirical results on synthetic/real datasets show that DCRF is essentially as accurate as other CRF variants, but is many times faster to train.
Citation
C. Lee, R. Greiner, O. Zaiane. "Efficient Spatial Classification Using Decoupled Conditional Random Field". European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, (ed: Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou), pp 272-283, September 2006.| Keywords: | |
| Category: | In Conference |
| Web Links: | Springer |
BibTeX
@incollection{Lee+al:PKDD06,
author = {Chi-Hoon Lee and Russ Greiner and Osmar R. Zaiane},
title = {Efficient Spatial Classification Using Decoupled Conditional Random
Field},
Editor = {Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou},
Pages = {272-283},
booktitle = {European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD)},
year = 2006,
}Last Updated: January 30, 2020Submitted by Sabina P
