2010 Fiscal Year Final Research Report
Construction and evaluation of non-Gaussian spatio-temporal models through the use of gradient maps
| Project/Area Number |
19700258
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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 |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SEI Tomonari The University of Tokyo, 大学院・情報理工学系研究科, 助教 (20401242)
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| Project Period (FY) |
2007 – 2010
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| Keywords | 勾配写像 / 凸最適化問題 / 輸送問題 / 方向統計学 |
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| Research Abstract |
For data in the form of function of time and space, we proposed a statistical model to extract complex structures behind the data. We used the gradient map as a special transformation. In particular, we apply a sparse estimation method, which is an analogy of recently developed methodology in machine learning theory. Furthermore, we generalize our models to those of directional data. We also clarify a theoretical merit of our models from the viewpoint of loss of estimation.
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