Sparse estimation via regularization and its application to ultra high-dimensional data

2014 Fiscal Year Final Research Report

• Project/Area Number: 24700277
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Statistical science
• Research Institution: Osaka University
• Principal Investigator: HIROSE Kei 大阪大学, 基礎工学研究科, 助教 (40609806)
• Research Collaborator: YAMAMOTO Michio 京都大学, 医学(系)研究科(研究院), 助教 (50721396)
• Project Period (FY): 2012-04-01 – 2015-03-31
• Keywords: スパース推定 / L1正則化 / モデル選択 / 因子分析

Outline of Final Research Achievements

Sparse estimation via L1 regularization enables us to analyze the ultra high-dimeniosnal data, and it has been rapidly developed in the recent years. I made two research achievements in this project. First, I developed an efficient algorithm that estimates the tuning parameter in sparse regression modeling. Second, I proposed the L1 regularization procedure in the factor analysis model, and investigated the relationship between the traditional rotation technique and regularization procedure. I showed that the regularization is viewed as the generalization of the rotation method. I have also made a free software package in R (msgps and fanc).

Free Research Field

統計科学