| Co-Investigator(Kenkyū-buntansha) |
竹田 晃人 茨城大学, 工学部, 准教授 (70397040)
渡邊 澄夫 東京工業大学, 情報理工学院, 教授 (80273118)
坂田 綾香 統計数理研究所, モデリング研究系, 助教 (80733071)
井上 純一 北海道大学, 情報科学研究科, 准教授 (30311658)
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| Budget Amount *help |
¥53,690,000 (Direct Cost: ¥41,300,000、Indirect Cost: ¥12,390,000)
Fiscal Year 2017: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2016: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2015: ¥11,960,000 (Direct Cost: ¥9,200,000、Indirect Cost: ¥2,760,000)
Fiscal Year 2014: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2013: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
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| Outline of Final Research Achievements |
The objective of this project is to develop and systemize methods of multivariate statistics that extract sparse structures lying behind observed data, utilizing the notion of information quantities. In general, one can systematically formulate the statistical-model-based information extraction as optimization problems concerning the information quantities. However, such methods are often computationally difficult to perform in practice. In this project, we intended to practically overcome this difficulty by employing methods and notions of statistical mechanics. By analyzing various concrete models, we aimed to construct a methodology for "systematic" and "practically performable" sparse modeling. Our achievements include performance analysis and algorithm development for various problems that arise in 1) compressive sensing, 2) latent variable models, and 3) issues of model selection.
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