TY - GEN
T1 - Learning and Estimation of Latent Structural Models Based on between-Data Metrics
AU - Mikawa, Kenta
AU - Kobayashi, Manabu
AU - Goto, Masayuki
AU - Hirasawa, Shigeichi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Numbers 19K04914 and 21H04600.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the development of information technology, a wide variety of data have been accumulated, and there are many methods for analyzing such data. In this study, we model the input data and the metrics between the data based on the assumption that each metric is generated from a continuous latent variable. Specifically, we assume that the input data are generated using low-dimensional latent variables and their projection matrices. We describe a method for estimating the latent variables. Because the generative model defined in this study cannot obtain the Q function analytically, we use the Monte Carlo EM algorithm to approximate the Q function and investigate an efficient parameter estimation method. Experiments using artificial data and the 20 newsgroups dataset demonstrate the effectiveness of the proposed method.
AB - With the development of information technology, a wide variety of data have been accumulated, and there are many methods for analyzing such data. In this study, we model the input data and the metrics between the data based on the assumption that each metric is generated from a continuous latent variable. Specifically, we assume that the input data are generated using low-dimensional latent variables and their projection matrices. We describe a method for estimating the latent variables. Because the generative model defined in this study cannot obtain the Q function analytically, we use the Monte Carlo EM algorithm to approximate the Q function and investigate an efficient parameter estimation method. Experiments using artificial data and the 20 newsgroups dataset demonstrate the effectiveness of the proposed method.
KW - continuous latent variable
KW - dimensionality reduction
KW - Monte Carlo EM algorithm
UR - http://www.scopus.com/inward/record.url?scp=85142746995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142746995&partnerID=8YFLogxK
U2 - 10.1109/SMC53654.2022.9945506
DO - 10.1109/SMC53654.2022.9945506
M3 - Conference contribution
AN - SCOPUS:85142746995
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3113
EP - 3118
BT - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Y2 - 9 October 2022 through 12 October 2022
ER -