Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8585
Title: The Infinite-Order Conditional Random Field Model for Sequential Data Modeling
Authors: Chatzis, Sotirios P. 
Demiris, Yiannis 
Keywords: Conditional random field
Mean-field principle
Sequence memoizer
Sequential data
Issue Date: Oct-2013
Publisher: IEEE
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, Volume 35, Issue 6, pages 1523 - 1534
Abstract: Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF ($({rm CRF}^{infty })$) model is experimentally demonstrated.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/8585
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2012.208
Rights: © Copyright IEEE
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