Cluster Selection Based on Coupling for Gaussian Mean Fields

Authors:
Yarui Chen;Shizhong Liao
Affiliations:
School of Computer Science and Technology, Tianjin University, Tianjin, P.R. China 300072;School of Computer Science and Technology, Tianjin University, Tianjin, P.R. China 300072
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

Citing 8
Cited 0

Factorial Hidden Markov Models

Machine Learning - Special issue on learning with probabilistic representations
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Multiclass Spectral Clustering

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On the choice of regions for generalized belief propagation

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graph partition strategies for generalized mean field inference

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)

Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Mean field theory for sigmoid belief networks

Journal of Artificial Intelligence Research
A generalized mean field algorithm for variational inference in exponential families

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gaussian mean field is an important paradigm of cluster-based variational inference, and its cluster selection is critical to the tradeoff between the variational accuracy and the computational complexity of cluster-based variational inference. In this paper, we explore a coupling based cluster selection method for Gaussian mean fields. First, we propose the model coupling and the quasi-coupling concepts on Gaussian Markov random field, and prove the coupling-accuracy theorem for Gaussian mean fields, which regards the quasi-coupling as a cluster selection criterion. Then we design a normalized cluster selection algorithm based on the criterion for Gaussian mean fields. Finally, we design numerical experiments to demonstrate the validity and efficiency of the cluster selection method and algorithm.