| Co-Investigator(Kenkyū-buntansha) |
田中 和之 東北大学, 情報科学研究科, 教授 (80217017)
村田 昇 早稲田大学, 理工学術院, 教授 (60242038)
井上 真郷 早稲田大学, 理工学術院, 教授 (70376953)
永田 賢二 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (10556062)
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| Budget Amount *help |
¥94,120,000 (Direct Cost: ¥72,400,000、Indirect Cost: ¥21,720,000)
Fiscal Year 2017: ¥17,810,000 (Direct Cost: ¥13,700,000、Indirect Cost: ¥4,110,000)
Fiscal Year 2016: ¥18,070,000 (Direct Cost: ¥13,900,000、Indirect Cost: ¥4,170,000)
Fiscal Year 2015: ¥18,200,000 (Direct Cost: ¥14,000,000、Indirect Cost: ¥4,200,000)
Fiscal Year 2014: ¥18,070,000 (Direct Cost: ¥13,900,000、Indirect Cost: ¥4,170,000)
Fiscal Year 2013: ¥21,970,000 (Direct Cost: ¥16,900,000、Indirect Cost: ¥5,070,000)
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| Outline of Final Research Achievements |
The sparse modeling team (B01-2) sets three tasks. Task 1 is applications of Bayesian spectral decomposition method to actual data. We developed a noise variance estimation method and a fast calculation method using L1 regularization and verified its effectiveness with actual data. In task 2, we developed a basis estimation and selection method using Sp-DMD for time series data, and applied it to actual data and verified its effectiveness. In task 3, a method of evaluating the appropriateness of the basis combination using an exhaustive search was developed, and this method was applied to actual data and the effectiveness was verified. Through research on these three tasks, we developed a universal method to extract latent structures using SpM and verified its effectiveness by actual data.
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