Statistical Machine Learning with Heterogeneous Auxiliary Information

2015 Fiscal Year Final Research Report

• Project/Area Number: 25870322
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Statistical science; Intelligent informatics
• Research Institution: Gifu University
• Principal Investigator: SHIGA Motoki 岐阜大学, 工学部, 助教 (20437263)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 行列分解 / クラスタ解析 / スパース正則化 / 変分ベイズ学習

Outline of Final Research Achievements

Recent developments of measuring engineering enable us to simultaneously monitor multiple variables and then the common size of datasets has been increasing. Thus finding essential simple rules hidden in such huge datasets becomes important. This research project has developed efficient clustering and matrix/tensor factorization methods by combining auxiliary information provided from databases and experts. Among a lot of data structures of auxiliary information, this project focused on auxiliary group and network structures. These results were also applied to data analysis on genome science, medical research, and material science.

Free Research Field

統計的機械学習