April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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214
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第八章：降維
但是這個隱性的假設不一定成立
，
例如
，
在圖
8-6
的下列中
，
決策邊界位於
x
1
= 5。
這個
決策邊界在原本的
3D
空間看起來非常簡單
（
一個垂直的平面
），
但是在展開的流形中看
起來複雜多了
（
四條獨立的線段
）。
總之
，
在訓練模型之前將訓練組降維通常可以提升訓練速度
，
但不一定會導致更好或更簡
單的解
，
一切依資料組而定
。
希望你現在已經瞭解什麼是維數災難
，
以及降維演算法如何對抗它
，
尤其是流形假設成立
時
。
本章接下來將介紹一些最熱門的演算法
。
圖
8-6
低維空間的決策邊界不一定比較簡單
PCA
主成分分析
（Principal Component Analysis，PCA）
是當今最熱門的降維演算法
。
它會先找
出最接近資料的超平面
，
再將資料投射到它上面
，
如圖
8-2
所示
。
PCA
|
215
保留變異度
我們必須先選出正確的超平面
，
才能將訓練組投射到低維超平面
。
例如
，
圖
8-7
的左圖是
個簡單的
2D
資料組
，
裡面有三個軸線
（
也就是
1D
超平面
）。
右圖是將資料集投射到各軸
的結果
。
你可以看到
，
投射到實線可保留最大的變異度
，
但投射到點虛線只保留極少的變
異度
，
而投到到短線虛線可保留以上兩者之間的變異度
。
圖
8-7
選擇投射的子空間
能夠保留最大變異度的軸應該是最合理的選項
，
因為它損失的資訊極可能比其他投射方式
還要少
。
支持這個選擇的另一種說法是 ...
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and much more.
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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.