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矢田 和善 研究室
Showing the original Japanese · View translation
高次元の小標本データを多変量解析と漸近理論で探る研究室。
AboutAbout the lab
高次元のデータの性質を、統計の理論で調べる。
矢田研究室は、
研究分野は、
数学学位
Key terms
- 高次元小標本データ
- 測定する
項目の 数に 比べて、 サンプルの 数が 少ない データ - 漸近理論
- データの
数や 項目の 数が 大きくなる ときの 統計手法の 振る 舞いを、 数学で 調べる 理論 - 多変量解析
- 多くの
項目を もつデータを、 まとめて 統計的に 分析する 手法の 総称 - 主成分分析
- たくさんの
変数が 持つ情報を、 少ない 数の 合成変数に まとめて 要約する 方法 - クラスター分
析 - 似ている
データどうしを、 まとまり （クラスター）に 分ける 方法
ResearchResearch themes
矢田研究室は、
*Illustrative image高次元データのクラスタリング
k-means法や
共分散・相関の推定と検定
項目どうしの
*Illustrative image主成分分析と外れ値の検出
多数の
MembersFaculty
- Professor矢田 和善
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Climate data analysis using tSNE method” Orlovma / CC BY 4.0 (cropped)
- 高次元データの
クラスタリング: “Spectral clustering vs k means” BogdanShevchenko / CC BY-SA 4.0 (cropped) - 主成分分析と
外れ値の 検出: “GreenAmongRed” Pereru / CC BY-SA 3.0 (cropped) - Related lab (
青嶋研究室): “Mechanical-calculator-Brunsviga-15-01” CEphoto, Uwe Aranas / CC BY-SA 3.0 (cropped) - Related lab (大谷内 奈穂 研究室): “Jeffreys prior probability for the beta distribution - J. Rodal” Dr. J. Rodal / CC BY-SA 3.0 (cropped)
- Related lab (Scott Carnahan 研究室): “Math01” MathKnight ✡ (Talk) No machine-readable author provided. MathKnight assumed (based on copyright claims). / CC BY 2.5 (cropped)
- Related lab (小池 貴之 研究室): “Complex plot for derangement function re and im between -9 to 9” A2569875 / CC BY-SA 4.0 (cropped)
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