<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>预处理 on 111qqz's blog</title><link>https://111qqz.com/en/tags/%E9%A2%84%E5%A4%84%E7%90%86/</link><description>Recent content in 预处理 on 111qqz's blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2015-2026 111qqz</copyright><lastBuildDate>Thu, 06 Jul 2017 08:35:51 +0000</lastBuildDate><atom:link href="https://111qqz.com/en/tags/%E9%A2%84%E5%A4%84%E7%90%86/index.xml" rel="self" type="application/rss+xml"/><item><title>Deep Learning Tutorial - PCA and Whitening</title><link>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2017-07-06-deep-learning-tutorial-pca-and-whitening/</link><pubDate>Thu, 06 Jul 2017 08:35:51 +0000</pubDate><guid>https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/2017-07-06-deep-learning-tutorial-pca-and-whitening/</guid><description>&lt;p&gt;说下我自己的理解&lt;/p&gt;
&lt;p&gt;PCA：主成分分析，是一种预处理手段。对于n维的数据，通过一些手段，把变化显著的k个维度保留，舍弃另外n-k个维度。对于一些非监督学习算法，降低维度可以有效加快运算速度。而n-k个最次要方向的丢失带来的误差不会很大。&lt;/p&gt;</description></item></channel></rss>