◆◆◆ 第9回『Digital Brain Seminar』開催のご案内 ◆◆◆

数学者の廣瀬 慧(九州大学 教授)に「罰則化による高次元解釈可能因子分析」についてご発表いただきます。

9th Seminar

Speaker：Kei Hirose (Kyushu University)

Place:：Zoom (Please find the zoom link by the registration)

Date：2025/2/17 10:30〜 (JST)

Title：High-dimensional interpretable factor analysis via penalization

Abstract： Factor analysis is a statistical method for identifying latent factors from the correlation structures of high-dimensional data. It was originally developed for applications in social and behavioral sciences but has since been applied to various research fields, including the natural sciences. An advantage of factor analysis is that it leads to interpretable latent factors, enabling applications such as the identification of active brain regions in neuroscience. In this study, we propose a penalized maximum likelihood estimation method aimed at enhancing the interpretability of latent factors. In particular, the Prenet (Product-based elastic net) penalization allows for the estimation of a perfect simple structure, a desirable characteristic in the factor analysis literature. The usefulness of the proposed method is investigated through real data analyses. Finally, we discuss potential extensions and applications of the proposed method in neuroscience.