| Budget Amount *help |
¥16,250,000 (Direct Cost: ¥12,500,000、Indirect Cost: ¥3,750,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2016: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
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| Outline of Final Research Achievements |
For data analysis of simultaneous heterogeneous measurements, this project has developed statistical machine learning methods to identify essential information from those datasets. For example, an efficient feature selection with auxiliary information of a graph structure and an effective component analysis method with some constraints caused by measurement conditions have been developed. These methods were also applied to applications of gene selection related with a disease in molecular biology and microscopy data analysis to identify essential and local chemical components in material science and physics.
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