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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3.12
ICA
（独立成分分析）
91
3.12
ICA
（独立成分分析）
ラベルなしデータを用いる際の一般的な問題の
1
つとして、多数の独立した信号が混合されて特徴
量を構成していることが挙げられる。ICA（
Independent Component Analysis
：独立成分分析）を用い
ると、混合された信号を個別の成分に分離することができる。うまく分離ができると、分離して得た独
立成分の組み合わせで、元の特徴量をすべて再構成することができる。
ICA
は 、 信 号 処 理 タ ス ク（ 混
雑したコーヒーショップで録音された音声クリップから個人の声を特定するなど）によく用いられる。
下に
ICA
の使い方を示す。
# Independent Component Analysis
from sklearn.decomposition import FastICA
n_components = 25
algorithm = 'parallel'
whiten = True
max_iter = 100
random_state = 2018
fastICA = FastICA(n_components=n_components, algorithm=algorithm, \
whiten=whiten, max_iter=max_iter, random_state=random_state)
X_train_fastICA = fastICA.fit_transform(X_train)
X_train_fastICA ...
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