2007 Fiscal Year Final Research Report Summary
The second order mean field approximation of graphical models and its application to Bayesian inference
| Project/Area Number |
17500088
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
TAKAHASHI Haruhisa The University of Electro-Communications, Information and Communication Engineering, Professor (90135418)
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| Co-Investigator(Kenkyū-buntansha) |
MURAMATSU Masakazu The Unic. of Electro-Communications, Dept. of Computer Science, Professor (70266071)
堀田 一弘 電気通信大学, 電気通信学部, 助教 (40345426)
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| Project Period (FY) |
2005 – 2007
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| Keywords | Markov Random Field / Conditional Random Field / Mean Field Approximation / Phase Equation / Population Coding |
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| Research Abstract |
Markov random field (MRF) and its discriminative version have been shown useful for both biological analysis and practical applications. In biological analysis, the debate on neuronal correlations is now continuing in which the analysis of the probability P( r| s) of the neuronal response r conditional on a stimulus s is required, which could be modeled with MRF. In this context the importance of a parametric model for analyzing correlations by modeling joint probability P(r, s) is shown using Gibbs distribution.
Several approximation techniques have been proposed for computing state probabilities of MRFs, CRFs, including belief propagation, which is not applicable for MRFs in a general situation. Mean field approximation (MRF) is known as only the generally applicable approximation technique at present.
To improve the accuracy of the mean-field approximation several advanced techniques have been proposed. Since the better accuracy we attain, the more intricate equations we get into, it
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becomes hard to know the efficient training procedure. In fact the training procedure is known only for the naive mean-field approximation (NMF), which is not so sufficient for the approximation accuracy.
The achievement of this research is to have refined the mean field approximation to alleviate both the testing and learning time, and to have shown the efficient learning scheme for object recognition with the variational phasor mean field model (VPMF). The striking result is that our learning scheme shows comparable testing performance with SVM, despite using much smaller size of training data, and in addition the detection time and the training time are much smaller than SVM based face detection.
Performance evaluation of VPMF is given for approximation accuracy, the local minima, and a face recognition problems. We have also attained the conclusion that the correlation of population coding in neural networks is more powerful than just using only the mean firing rate.
Performance evaluation of VPMF is given for approximation accuracy, the local minima, and a face recognition problems. Less
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