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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3.7
MDS
（多次元尺度構成法）
85
# Isomap
from sklearn.manifold import Isomap
n_neighbors = 5
n_components = 10
n_jobs = 4
isomap = Isomap(n_neighbors=n_neighbors, \
n_components=n_components, n_jobs=n_jobs)
isomap.fit(X_train.loc[0:5000,:])
X_train_isomap = isomap.transform(X_train)
X_train_isomap = pd.DataFrame(data=X_train_isomap, index=train_index)
X_validation_isomap = isomap.transform(X_validation)
X_validation_isomap = pd.DataFrame(data=X_validation_isomap, \
index=validation_index)
scatterPlot(X_train_isomap, y_train, "Isomap")
図 3-11に、
Isomap
による
2
次元散布図を示す。
図 3-11 Isomapを用いた場合の観測点の分離
3.7
MDS
（多次元尺度構成法）
MDS（
MultiDimensional Scaling
：多次元尺度構成法）は、非線形次元削減の
1
つ で 、元 の デ ー
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