Theories of structured estimation methods for large scale data and their applications
| Project/Area Number |
25730013
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo (2017)
Tokyo Institute of Technology (2013-2016) |
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Principal Investigator |
SUZUKI Taiji 東京大学, 大学院情報理工学系研究科, 准教授 (60551372)
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| Project Period (FY) |
2013-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 構造的正則化 / テンソルモデリング / ベイズ推定 / 再生核ヒルベルト空間 / ガウシアンプロセス / 確率的最適化 / 高次元統計 / 統計的学習理論 / 機械学習 / 統計的学習 / 深層学習 / スパース推定 / 確率密度比 / ビッグデータ / 低ランクテンソル推定 / 交互最適化 / 交互方向乗数法 / ガウシアンプロセス事前分布 / ベイズ統計 / 交互方向定数法 / 低ランク行列 |
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| Outline of Final Research Achievements |
Recently, the size of dataset is getting larger and larger in several areas. Moreover, such data often contains various structures. To deal with such a complicated and large data, we have focused on structured sparsity and developed new estimation methods and computational methods in a comprehensive manner. Specifically, we have proposed a new stochastic optimization method called stochastic alternating direction method of multipliers that work efficiently for structured regularization methods. We also studied so called tensor modeling and proposed some estimators that satisfy mini-max optimality. Through the above mentioned problems, we have studied theories and applications in a comprehensive manner.
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Report
(6 results)
Research Products
(101 results)
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[Journal Article] Structure Learning of Partitioned Markov Networks2016
Author(s)
Song Liu, Taiji Suzuki, Masashi Sugiyama, and Kenji Fukumizu
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Journal Title
Proceedings of Machine Learning Research (The 33rd International Conference on Machine Learning)
Volume: 48
Pages: 439-448
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Peer Reviewed / Open Access / Int'l Joint Research / Acknowledgement Compliant
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[Book] 共立出版2014
Author(s)
Trevor Hastie, Robert Tibshirani, Jerome Friedman (原著), 杉山 将, 井手 剛, 神嶌 敏弘, 栗田 多喜夫, 前田 英作（編）, 鈴木大慈ほか（訳）
Total Pages
888
Publisher
統計的学習の基礎：データマイニング・推論・予測
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