Learning and Estimation of Latent Structural Models Based on between-Data Metrics
Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
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.