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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4.5
ガウス型ランダム射影を用いた異常検出
105
図4-8 カーネル PCA による 27 主成分を用いた場合の結果
図4-8からわかるように、この結果は通常の
PCA
やスパース
PCA
よりもはるかに悪い。試してみる
価値はあったが、ほかにも良い方法があるので、この手法を不正検出に使うことはないだろう。
ここでは
SVD
を用いた異常検出システムは示さない。
SVD
と通常の
PCA
の結果はほとんど
同じだったからだ。これは
PCA
と
SVD
は密接な関係にあるからで、予想された通りだ。
SVD
はスキップして、次はランダム射影を用いた異常検出を見てみよう。
4.5
ガウス型ランダム射影を用いた異常検出
ガウス型ランダム射影を用いて、不正検出システムを構築してみよう。この手法では、成分の数を
指定することもできるし、
Johnson-Lindenstrauss
の補題に基づいて導出される、埋め込みの質を制御
する
eps
パラメータを指定することもできるのだった。
ここでは、成分の数を明示的に設定することにしよう。ガウス型ランダム射影の学習は非常に速い
ので訓練セットすべてを使うことにする。
スパース
PCA
の場合と同様に、
scikit-learn
はこの手法に対する
inverse_transform
関数を提供
していないので、独自に実装する必要がある。
# Gaussian Random Projection
from sklearn.random_projection import GaussianRandomProjection
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.