A machine learning based approach to analysing latent substructure of graph data

2016 Fiscal Year Final Research Report

• Project/Area Number: 26730120
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Intelligent informatics
• Research Institution: Nagoya Institute of Technology (2015-2016); Kyoto University (2014)
• Principal Investigator: Karasuyama Masayuki 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (40628640)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 機械学習 / グラフ / 多様体学習 / 半教師学習

Outline of Final Research Achievements

A variety of data can be represented as a graph. For statistical data analysis based on graphs, it is important to consider setting appropriate parameters of graphs and extracting informative structure. This study focuses on a methodological study of ``machine learning'' based on graph data. In particular, the label estimation problem on a graph and the important subgraph identification problem have been considered. For example, accurate label estimation methods and scalable subgraph identification methods are required by biological data analysis.

Free Research Field

機械学習