January 2020
Beginner to intermediate
728 pages
10h 26m
Japanese
Content preview from データサイエンス設計マニュアル
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240 8 章 線形代数
図 8 -16 PC
A は、黒い点を直交する軸に射影し、それを回転して赤い代替表現を生成する（左）。個々の
成分の値は、適切な軸に個々の点を射影して求める（右）
ベクトルのうち相関性の高いものを 1 つにまとめて次元数を減らしながら、それらの特徴ベクトルを線形結
合して新しい特徴ベクトルを作っていく。統計因子分析は、分散の大部分を説明する最も重要（相関という
尺度で）な直交次元を明らかにしてくれる。
点の集合の基本構造は、比較的少数の成分で十分捉えることができる。未処理の部分はノイズの可能性が
高いので、データから取り除いた方がかえって良い場合が多い。PCA（または SVD）で次元削減すると、単
に次元数が減るだけでなく、もとのデータよりもクリーンなデータが得られる。
今後の課題
PCA と S
VD は、アプローチは異なるが、本質的に同じものを計算する。特徴行列の低次元行列
による近似ではどちらも同じように効果を発揮する。
8.6 私の体験談から：ヒューマンファクター
共著書の Who’s Bigger? のために歴史的偉人の分析をしていたときに、PCA や SVD といった次元削減手
法の威力に初めて触れて衝撃を受けた。4.7 節の「私の体験談から」でも紹介したように、我々は Wikipedia
の構造とコンテンツを分析し、英語版に掲載されている 80 万人分以上の項目から PageRank や記述の長さ
といった 6 種類の特徴を抽出することにした。これにより、個々の人物は
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.