A study on latent structural models for binary relational data with attribute information

被引:0
作者
Mikawa, Kenta [1 ]
Kobayashi, Manabu [2 ]
Sasaki, Tomoyuki [3 ]
Manada, Akiko [4 ]
机构
[1] Tokyo City Univ, Dept Informat Syst, 3-3-1 Ushikubonishi,Tsuzuki Ku, Yokohama, Kanagawa 2248551, Japan
[2] Waseda Univ, Ctr Data Sci, 1-6-1 Nishiwaseda,Shinjuku Ku, Tokyo 1698050, Japan
[3] Shonan Inst Technol, Dept Informat, 1-1-25 Tsujido Nishikaigan, Fujisawa, Kanagawa 2518511, Japan
[4] Nagaoka Univ Technol, Dept Elect Elect & Informat Engn, 1603-1 Kamitomioka, Nagaoka, Niigata 9402188, Japan
来源
IEICE NONLINEAR THEORY AND ITS APPLICATIONS | 2024年 / 15卷 / 02期
关键词
statistical relational learning; latent structural model; Monte Carlo EM algo- rithm; Laplace approximation;
D O I
10.1587/nolta.15.335
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This study focuses on relational data obtained through object relations. Traditional analysis of relational data often ignores attribute information. Therefore, Mikawa et al. proposed a method to estimate the latent structure of continuous relational data using a generative model and parameter estimation. However, real-world relational data can be discrete, and therefore, we propose a new model for binary relational data using a generative model based for parameter estimation. We also clarify the effectiveness of the proposed model through simulation experiments using artificial data and real data.
引用
收藏
页码:335 / 353
页数:19
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