Nakayama Yugo 中山優吾

Researcher Number	40884169
Other IDs	https://orcid.org/0000-0001-7321-2141
Affiliation (Current)	2025: 京都大学, 情報学研究科, 助教
Affiliation (based on the past Project Information) *help	2020 – 2022: 京都大学, 情報学研究科, 助教
Review Section/Research Field	Principal Investigator Basic Section 12040:Applied mathematics and statistics-related / 0201:Algebra, geometry, analysis, applied mathematics,and related fields Except Principal Investigator Medium-sized Section 60:Information science, computer engineering, and related fields
Keywords	Principal Investigator 高次元小標本 / 機械学習 / 高次元データ / 高次元統計解析 / 深層学習 / 特徴量選択 / クラスタリング / 外れ値検出 / 非線形 Except Principal Investigator 高次元小標本 … More / 深層学習 / 高次元統計解析 / 時空間データ / 高次元データ Less

深層学習の数学的原理：高次元統計解析との接点の解明Principal Investigator
- Principal Investigator
  
  中山優吾
- Project Period (FY)
  2021 – 2022
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 12040:Applied mathematics and statistics-related
- Research Institution
  Kyoto University
High-dimension, low-sample-size asymptotic theory for nonlinear feature selectionPrincipal Investigator
- Principal Investigator
  
  Nakayama Yugo
- Project Period (FY)
  2020 – 2022
- Research Category
  
  Grant-in-Aid for Research Activity Start-up
- Review Section
  
  0201:Algebra, geometry, analysis, applied mathematics,and related fields
- Research Institution
  Kyoto University
Innovative Developments of Theories and Methodologies for Large Complex Data
- Principal Investigator
  
  青嶋誠
- Project Period (FY)
  2020 – 2024
- Research Category
  
  Grant-in-Aid for Scientific Research (A)
- Review Section
  
  Medium-sized Section 60:Information science, computer engineering, and related fields
- Research Institution
  University of Tsukuba

All 2022 2021 2020 2019

All Journal Article Presentation

[Journal Article] Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings2021
- Author(s)
  Nakayama Yugo、Yata Kazuyoshi、Aoshima Makoto
- Journal Title
  
  Journal of Multivariate Analysis
  
  Volume: 185 Pages: 104779-104779
- DOI
  10.1016/j.jmva.2021.104779
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-18K03409, KAKENHI-PROJECT-19K22837, KAKENHI-PROJECT-20H00576, KAKENHI-PROJECT-20K22305
[Journal Article] Support vector machine and optimal parameter selection for high-dimensional imbalanced data2020
- Author(s)
  Nakayama Yugo
- Journal Title
  
  Communications in Statistics - Simulation and Computation
  
  Volume: 1 Issue: 11 Pages: 1-16
- DOI
  10.1080/03610918.2020.1813300
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K22305
[Journal Article] Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings2019
- Author(s)
  Nakayama Yugo、Yata Kazuyoshi、Aoshima Makoto
- Journal Title
  
  Annals of the Institute of Statistical Mathematics
  
  Volume: － Issue: 5 Pages: 1-30
- DOI
  10.1007/s10463-019-00727-1
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-15H01678, KAKENHI-PROJECT-19J10175, KAKENHI-PROJECT-18K03409, KAKENHI-PROJECT-20K11701, KAKENHI-PROJECT-19K22837, KAKENHI-PROJECT-20H00576
[Presentation] 高次元主成分分析における頑健性について2022
- Author(s)
  中山優吾, 矢田和善, 青嶋誠
- Organizer
  日本数学会秋季総合分科会
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] Multiple outlier detection test with PCA in high-dimension, low-sample-size settings2022
- Author(s)
  中山優吾, 矢田和善, 青嶋誠
- Organizer
  2022年度統計関連学会連合大会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] 高次元主成分分析における頑健性について2022
- Author(s)
  中山優吾, 矢田和善, 青嶋誠
- Organizer
  日本数学会2022年度秋季総合分科会
- Data Source
  KAKENHI-PROJECT-21K13833
[Presentation] Multiple outlier detection test with PCA in high-dimension, low-sample-size settings2022
- Author(s)
  中山優吾, 矢田和善, 青嶋誠
- Organizer
  統計関連学会連合大会
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 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2022
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  多様な高次元モデルの理論と方法論：最前線の動向
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] Test for outlier detection by high-dimensional PCA2022
- Author(s)
  Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
- Organizer
  The 5th International Conference on Econometrics and Statistics
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K13833
[Presentation] 高次元主成分スコアに基づく異常値の検出法2022
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  日本数学会2022年度年会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] 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2022
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  科研費シンポジウム「多様な高次元モデルの理論と方法論：最前線の動向」
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] Test for outlier detection by high-dimensional PCA2022
- Author(s)
  Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
- Organizer
  The 5th International Conference on Econometrics and Statistics
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] Asymptotic properties of high-dimensional kernel PCA and its applications2021
- Author(s)
  Nakayama Y., Yata K., Aoshima M.
- Organizer
  International Symposium on New Developments of Theories and Methodologies for Large Complex Data
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元における重み付き判別分析とデータ変換法について2021
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  2021年度統計関連学会連合大会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] Asymptotic properties of high-dimensional kernel PCA and its applications2021
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  International Symposium on New Developments of Theories and Methodologies for Large Complex Data
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] 高次元におけるカーネル主成分分析の漸近的性質と異常値の検出への応用2021
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  日本数学会年会
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] Clustering by kernel PCA with Gaussian Kernel and tuning for high-dimensional data2021
- Author(s)
  Nakayama Y., Yata K., Aoshima M.
- Organizer
  The 4th International Conference on Econometrics and Statistics
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元におけるカーネル主成分分析の漸近的性質と異常値の検出への応用2021
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  日本数学会2021年度年会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] ガウシアンカーネルに基づく高次元データの分類問題2021
- Author(s)
  中山優吾
- Organizer
  2021年度秋季総合分科会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] ガウシアンカーネルに基づく高次元データの分類問題（特別講演）2021
- Author(s)
  中山優吾
- Organizer
  日本数学会秋季総合分科会
- Invited
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] Clustering by kernel PCA with Gaussian kernel and tuning for high-dimensional data2021
- Author(s)
  Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
- Organizer
  The 4th International Conference on Econometrics and Statistics
- Invited / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] Clustering by kernel principal component analysis for high-dimensional data2020
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  日本数学会秋季総合分科会
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元カーネル主成分分析に基づく異常値の検出2020
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  科研費シンポジウム「大規模複雑データの理論と方法論：最前線の動向と新たな展開」
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] 高次元小標本における異常値の検出2020
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  統計関連学会連合大会
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元データにおける異常値の検出について2020
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  科研費シンポジウム「機械学習・統計学・最適化の数理とＡＩ技術への展開」
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] Clustering by kernel principal component analysis for high-dimensional data2020
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  日本数学会 2020年度秋季総合分科会
- Data Source
  KAKENHI-PROJECT-20K22305
[Presentation] 高次元カーネル主成分分析に基づく異常値の検出2020
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  科研費シンポジウム「大規模複雑データの理論と方法論：最前線の動向と新たな展開」
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元データにおける異常値の検出について2020
- Author(s)
  中山優吾、矢田和善、青嶋誠
- Organizer
  科研費シンポジウム「機械学習・統計学・最適化の数理とAI技術への展開」
- Data Source
  KAKENHI-PROJECT-20H00576
[Presentation] 高次元小標本における異常値の検出2020
- Author(s)
  中山優吾，矢田和善，青嶋誠
- Organizer
  2020年度統計関連学会連合大会
- Data Source
  KAKENHI-PROJECT-20K22305

1. 青嶋誠 (90246679)

# of Collaborated Projects: 1 results

# of Collaborated Products: 13 results
2. 矢田和善 (90585803)

# of Collaborated Projects: 1 results

# of Collaborated Products: 13 results
3. 星野伸明 (00313627)

# of Collaborated Projects: 1 results

# of Collaborated Products: 0 results
4. 塩濱敬之 (40361844)

# of Collaborated Projects: 1 results

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5. 鈴木大慈 (60551372)

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6. 石井晶 (20801161)

# of Collaborated Projects: 1 results

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7. 江頭健斗 (20979869)

# of Collaborated Projects: 1 results

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8. 荒木由布子 (80403913)

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9. 川野秀一 (50611448)

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10. 蛭川潤一 (10386617)

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11. 松田安昌 (10301590)

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12. 田畑耕治 (30453814)

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13. 片山翔太 (50742459)

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14. 植木優夫 (10515860)

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15. 小森理 (60586379)

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16. 金森敬文 (60334546)

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17. 松井秀俊 (90633305)

# of Collaborated Projects: 1 results

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Nakayama Yugo 中山 優吾

Research Projects

Research Products

Co-Researchers

深層学習の数学的原理：高次元統計解析との接点の解明Principal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

High-dimension, low-sample-size asymptotic theory for nonlinear feature selectionPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Innovative Developments of Theories and Methodologies for Large Complex Data

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings2021

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Support vector machine and optimal parameter selection for high-dimensional imbalanced data2020

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings2019

Author(s)

Journal Title

DOI

Data Source

[Presentation] 高次元主成分分析における頑健性について2022

Author(s)

Organizer

Data Source

[Presentation] Multiple outlier detection test with PCA in high-dimension, low-sample-size settings2022

Author(s)

Organizer

Data Source

[Presentation] 高次元主成分分析における頑健性について2022

Author(s)

Organizer

Data Source

[Presentation] Multiple outlier detection test with PCA in high-dimension, low-sample-size settings2022

Author(s)

Organizer

Data Source

[Presentation] 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2022

Author(s)

Organizer

Data Source

[Presentation] Test for outlier detection by high-dimensional PCA2022

Author(s)

Organizer

Data Source

[Presentation] 高次元主成分スコアに基づく異常値の検出法2022

Author(s)

Organizer

Data Source

[Presentation] 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2022

Author(s)

Organizer

Data Source

[Presentation] Test for outlier detection by high-dimensional PCA2022

Author(s)

Organizer

Data Source

[Presentation] Asymptotic properties of high-dimensional kernel PCA and its applications2021

Author(s)

Organizer

Data Source

[Presentation] 高次元における重み付き判別分析とデータ変換法について2021

Author(s)

Organizer

Nakayama Yugo 中山優吾