DocumentCode :
1007177
Title :
A comparison of algorithms for inference and learning in probabilistic graphical models
Author :
Frey, Brendan J. ; Jojic, Nebojsa
Author_Institution :
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont., Canada
Volume :
27
Issue :
9
fYear :
2005
Firstpage :
1392
Lastpage :
1416
Abstract :
Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy" belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.
Keywords :
graph theory; inference mechanisms; iterative methods; learning (artificial intelligence); optimisation; probability; variational techniques; Gibbs sampling; expectation maximization algorithm; inference algorithm; iterated conditional modes; learning algorithm; mean field method; probabilistic graphical model; sum-product algorithm; variational techniques; Artificial intelligence; Character recognition; Computer vision; Face detection; Graphical models; Inference algorithms; Large-scale systems; Learning; Pattern classification; Uncertainty; Bayesian methods; Bayesian networks; Bethe free energy.; EM algorithm; Gibbs free energy; Gibbs sampling; Index Terms- Graphical models; free energy; learning; loopy belief propagation; mean field; probabilistic inference; probability models; reasoning; sum-product algorithm; variational techniques; Algorithms; Artificial Intelligence; Computer Graphics; Computer Simulation; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2005.169
Filename :
1471706
Link To Document :
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