Deepening and applications of sparse modeling by approaches of semiparametric Bayesian inference

2017 Fiscal Year Final Research Report

• Project Area: Initiative for High-Dimensional Data-Driven Science through Deepening of Sparse Modeling
• Project/Area Number: 25120012
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: The Institute of Statistical Mathematics
• Principal Investigator: Fukumizu Kenji 統計数理研究所, 数理・推論研究系, 教授 (60311362)
• Co-Investigator(Kenkyū-buntansha): 鈴木 大慈 東京大学, 大学院情報理工学系研究科, 准教授 (60551372) ; 西山 悠 電気通信大学, 大学院情報理工学研究科, 助教 (60586395) ; 冨岡 亮太 東京大学, 情報理工学(系)研究科, 助教 (70518282)
• Co-Investigator(Renkei-kenkyūsha): NISHIYAMA Yu 電気通信大学, 大学院情報理工学研究科, 助教 (60586395) ; LIU Song 統計数理研究所, 統計的機械学習研究センター, 特任助教 (80760579)
• Project Period (FY): 2013-06-28 – 2018-03-31
• Keywords: スパースモデリング / セミパラメトリック / ベイズ推論 / 最適化 / アルゴリズム

Outline of Final Research Achievements

Filtering problems aims at estimating the current unobserved state variable from the unknown dynamics of the unobserved state variables and indirect observations. We consider filtering under the assumption that the observation model is uncertain and not able to be modeled easily, and proposed effective algorithms in such difficult situations. We confirmed the advantage of the proposed algorithms over existing relevant methods. We also studied fast methods for complex sparse modeling, and proposed an optimization methods that achieves the best convergence rate theoretically.

Free Research Field

機械学習，数理統計