April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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106
4
章 異常検出
X_train_GRP = GRP.fit_transform(X_train)
X_train_GRP = pd.DataFrame(data=X_train_GRP, index=X_train.index)
scatterPlot(X_train_GRP, y_train, "Gaussian Random Projection")
図4-9に、ガウス型ランダム射影を用いた場合の散布図を示す。図 4-10 に、ガウス型ランダム射影
の結果を示す。
図4-9 ガウス型ランダム射影による27主成分を用いた場合の観測点の分離
図 4-10 ガウス型ランダム射影による 27 成分を用いた結果
結果は良くない。ガウス型ランダム射影を不正検出に使うことはないだろう。
4.6
スパースランダム射影を用いた異常検出
スパースランダム射影を用いた不正検出システムを構築してみよう。
4.6
スパースランダム射影を用いた異常検出
107
ここでも
eps
パラメータを設定するのではなく、成分の数を指定した。ガウス型ランダム射影の場
合と同様に、自前で実装した
inverse_transform
を用いて、スパースランダム射影で得た成分から、
元の次元を再構築する。
# Sparse Random Projection ...
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