April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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数据降维：使用
PCA
挤压数据
｜
93
6.7
小结
关于
PCA
的讨论到此结束。关于
PCA
我们应该记住的主要两点是它的原理（线性投影）和
目标（最大化投影数据的方差）。
PCA
的解要用到协方差矩阵的特征值分解，它与数据矩阵
的
SVD
联系非常紧密。还可以这样形象地理解
PCA
：将数据挤入一张尽量松软的薄饼中。
PCA
是一种模型驱动的特征工程方法。（只要看到一个目标函数，就应该马上猜想它的背
后可能隐藏着一个模型。）其中的建模假设是方差能够很好地表示数据中包含的信息。同
样，模型要找出的是特征之间的线性相关性。在若干应用中可以使用
PCA
来减少相关性
或找出输入中常见的因子。
PCA
是一种广为人知的数据降维方法，但它也有自身的局限性，比如高昂的计算成本和不
能解释的结果。它适合应用在预处理阶段，特别是特征之间存在线性相关性的时候。
当作为一种消除线性相关性的方法时，
PCA
还可以和白化方法结合使用。它的“表亲”
ZCA
可以用可解释的方式对数据进行白化，但不能降低数据维度。
6.8
参考文献
Bell, Anthony J. and Terrence J. Sejnowski. Edges Are the
“
Independent Components
”
of Natural
Scenes [G]. Advances in Neural Information Processing Systems 9 (1996): 831–837.
Coates, Adam, Andrew Y. Ng, and Honglak Lee. ...
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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.