Nakayama Yugo

ナカヤマユウゴ | Nakayama Yugo

Affiliation and department：

Research field (1)： Statistical science

Research keywords (3)：機械学習 , 高次元統計解析 , 多変量解析

Research theme for competitive and other funds (4)：

2021 - 2026 Mathematical Principles of Deep Learning: Clarifying the Interface with High-Dimensional Statistical Analysis
2020 - 2025 Innovative Developments of Theories and Methodologies for Large Complex Data
2020 - 2022 High-dimension, low-sample-size asymptotic theory for nonlinear feature selection
2019 - 2020 Kernel principal component analysis in high dimension, low sample size and its applications

Papers (7)：

Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima. Test for high-dimensional outliers with principal component analysis. Japanese Journal of Statistics and Data Science. 2024
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima. Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings. Journal of Multivariate Analysis. 2021. 185. 104779-104779
Yugo Nakayama. Robust support vector machine for high-dimensional imbalanced data. Communications in Statistics - Simulation and Computation. 2021. 50. 5. 1524-1540
Yugo Nakayama. Support vector machine and optimal parameter selection for high-dimensional imbalanced data. Communications in Statistics - Simulation and Computation. 2020. 51. 11. 6739-6754
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima. Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings. Annals of the Institute of Statistical Mathematics. 2019. 72. 5. 1257-1286

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MISC (4)：

Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima. Soft-margin SVMs in the HDLSS context. Res. Inst. Math. Sci., Kyoto University. 2019. 2124. 44-55
A general framework of SVM in HDLSS settings. Res. Inst. Math. Sci., Kyoto University. 2018. 2091. 14-21
Asymptotic properties of support vector machines in HDLSS settings. Res. Inst. Math. Sci., Kyoto University. 2017. 2047. 10-18
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima. Consistency of SVM in high-dimension, low-sample-size context. Res. Inst. Math. Sci., Kyoto University. 2016. 1999. 1999. 17-27

Lectures and oral presentations (37)：

A simple heavy tailed cylindrical model and its applications
(CMStatistics 2023 2023)
Multiple outlier detection test with PCA in high-dimension, low-sample-size settings
(Japanese Joint Statistical Meeting 2022 2022)
Test for outlier detection by high-dimensional PCA
(The 5th International Conference on Econometrics and Statistics 2022)
高次元主成分スコアに基づく異常値の検出法
(日本数学会2022年度年会 2022)
高次元におけるカーネル主成分分析の漸近的性質とその応用
(多様な高次元モデルの理論と方法論:最前線の動向 2022)

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Education (3)：

2017 - 2020 University of Tsukuba Graduate School of Pure and Applied Sciences
2015 - 2017 University of Tsukuba Graduate School of Pure and Applied Sciences
2011 - 2015 University of Tsukuba School of Science and Engineering, College of Mathematics

Professional career (1)：

Work history (2)：

2023/04 - 現在 Nissan Motor Co., Ltd.
2020/04 - 2023/03 Kyoto University Graduate School of Informatics Assistant Professor

Awards (9)：

2020/03 - University of Tsukuba 2020 DEAN AWARD, GRADUATE SCHOOL OF PURE AND APPLIED SCIENCES: UNIVERSITY OF TSUKUBA
2019/06 - ABRAHAM WALD PRIZE in Sequential Analysis 2019
2019/03 - The Japan Statistical Society OUTSTANDING POSTER PRESENTATION AWARD Clustering for high-dimensional data by kernel PCA with the Gaussian kernel
2018/11 - The Mathematical Society of Japan Best Poster Presentation Award
2018/03 - THE JAPAN STATISTICAL SOCIETY SPRING MEETING OUTSTANDING POSTER PRESENTATION AWARD SVM with Gaussian kernel: Bias correction and tuning parameter for high-dimensional data

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Association Membership(s) (2)：

日本統計学会 , 日本数学会

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