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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92
3
章 次元削減
図 3-16 ICA による観測点の分離
3.13
まとめ
本章では、いくつかの次元削減アルゴリズムを紹介し、実際にデータに適用した。まずは
PCA
やラ
ンダム射影などの線形手法からはじめ、
Isomap
、多次元尺度構成法、局所線形埋め込み、
t-SNE
など
の非線形手法（多様体学習とも呼ばれる）に進んだ。さらに、辞書学習や
ICA
などの距離に基づかない
手法も紹介した。
次元削減は、データの背後にある構造をラベルなしに学習することで、データセット中の最も重要な
情報を比較的少数の次元数で捉えることができる。これらのアルゴリズムを
MNIST
数字データセット
に適用すると、わずか
2
次元で、画像を数字ごとに分離できることがわかった。
この結果は次元削減の威力を示すものだ。
「4章 異常検出」では、次元削減アルゴリズムを用いた教師なし学習応用システムを構築する。具
体的には、「2 章 機械学習プロジェクトのはじめから終わりまで」で導入した不正トランザクションを
真正のトランザクションから分離する不正検出システムを、ラベルを利用せずに構築する。
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.