Learning and Estimation of Latent Structural Models Based on between-Data Metrics

Mikawa, Kenta; Kobayashi, Minoru; Goto, Masayuki; Hirasawa, Shigeichi

doi:10.1109/smc53654.2022.9945506

Cited by 1 publication

(4 citation statements)

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“…al. have proposed a latent structural model of continuous relational data with attribution information and how to estimate the latent structure of the data [7]. The conventional model assumes that the relation y ij between the i-th and the j-th objects is generated from…”

Section: Latent Structural Models For Continuous Relational Data [7]mentioning

confidence: 99%

“…In our proposed model, we calculate the posterior distribution of z i and estimate the parameters using the Monte Carlo EM algorithm with samples generated from the posterior distribution. However, since the probability distribution of y ij follows a Bernoulli distribution, we cannot derive the exact posterior distribution like [7]. Therefore, we use Laplace approximation [17] to approximate the posterior distribution of z i .…”

Section: Laplace Approximationmentioning

confidence: 99%

“…al. have proposed a latent structural model of relational data and how to estimate the latent structure of the data [7]. The conventional model assumes that its relations are represented in continuous values and defines a generative model for relational data with attribute information.…”

Section: Introductionmentioning

confidence: 99%

“…2, our related studies, definitions of variables for our study and the Monte Carlo EM Algorithm which is one of the parameter estimation methods are introduced. In addition, the conventional model [7], which is a latent structural model of continuous relational data with attribution information and how to estimate the latent structure of the data, is explained. In Sect.…”

Section: Introductionmentioning

confidence: 99%

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

Mikawa,

Kobayashi,

Sasaki

et al. 2024

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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 on the Bernoulli distribution and the Monte Carlo Expectation-Maximization (EM) algorithm for parameter estimation. We also clarify the effectiveness of the proposed model through simulation experiments using artificial data and real data.

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Section: Latent Structural Models For Continuous Relational Data [7]mentioning

confidence: 99%

Section: Laplace Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations