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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84
3
章 次元削減
random_state = 2018
SRP = SparseRandomProjection(n_components=n_components, \
density=density, eps=eps, dense_output=dense_output, \
random_state=random_state)
X_train_SRP = SRP.fit_transform(X_train)
X_train_SRP = pd.DataFrame(data=X_train_SRP, index=train_index)
X_validation_SRP = SRP.transform(X_validation)
X_validation_SRP = pd.DataFrame(data=X_validation_SRP, index=validation_index)
scatterPlot(X_train_SRP, y_train, "Sparse Random Projection")
図 3-10 に、スパースランダム射影による
2
次元散布図を示す。
図 3-10 スパースランダム射影を用いた場合の観測点の分離
3.6
Isomap
データを高次元空間から低次元空間へ線形に射影するのではなく、非線形に次元削減を行う手法も
ある。これらの手法は多様体学習と総称される。
最も基本的な多様体学習手法は、Isomap（
isometric mapping
：等尺性マッピング）と呼ばれる手 ...
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