Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
A tutorial on learning with Bayesian networks
Learning in graphical models
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Independence is good: dependency-based histogram synopses for high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Chordal Graphs and Their Clique Graphs
WG '95 Proceedings of the 21st International Workshop on Graph-Theoretic Concepts in Computer Science
Learning with mixtures of trees
The Journal of Machine Learning Research
Word-sense disambiguation using decomposable models
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Fully dynamic algorithm for chordal graphs with O(1) query-time and O(n 2) update-time
Theoretical Computer Science
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In this paper, we present an efficient algorithm for performing stepwise selection in the class of decomposable models. We focus on the forward selection procedure, but we also discuss how backward selection and the combination of the two can be performed efficiently. The main contributions of this paper are (1) a simple characterization for the edges that can be added to a decomposable model while retaining its decomposability and (2) an efficient algorithm for enumerating all such edges for a given decomposable model in O(n2) time, where n is the number of variables in the model. We also analyze the complexity of the overall stepwise selection procedure (which includes the complexity of enumerating eligible edges as well as the complexity of deciding how to "progress"). We use the KL divergence of the model from the saturated model as our metric, but the results we present here extend to many other metrics as well.