| Budget Amount *help |
¥17,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥3,990,000)
Fiscal Year 2021: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2020: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2019: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2017: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Outline of Final Research Achievements |
An effective way to handle large amounts of data by machine learning is to reduce the data to the parameters of a probability distribution. In this project, we have been working on the development of machine learning for such data. Originally, there was a study of extending principal component analysis to distributional data, which had been developed by a project member. The significant contribution of this project was to extend it to a more flexible nonparametric framework, which was achieved through information geometry of Gaussian process regression and other methods. We have also been able to apply information geometry to neuroscience and geophysics through the application of matrix factorization.
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