2012 Fiscal Year Final Research Report
Study about the learning algorithm applicable to large-scale Markov Random Fields
| Project/Area Number |
22700232
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Kyoto University |
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Principal Investigator |
MAEDA Shin-ichi 京都大学, 大学院・情報学研究科, 助教 (20379530)
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| Project Period (FY) |
2010 – 2012
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| Keywords | マルコフ確率場 / 詳細釣り合い / group-coordinate descent / 因子化仮定 |
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| Research Abstract |
Learning and inference algorithms of the high-dimensional Markov Random Fields were developed for image processing and speech processing. In these application fields, Markov property is formed locally in spatial or temporal aspects. Group-coordinate descent was proposed for the optimization to exploit this locality. Since it enables to optimize the optimization problem without losing the convergence rate regardless of the dimensionality, it is applied to the real world problems such as image super-resolution and X-ray computed tomography.
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