<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cca on 桑峰</title><link>https://isangfeng.github.io/tags/cca/</link><description>Recent content in Cca on 桑峰</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><copyright>© 2026 桑峰</copyright><lastBuildDate>Wed, 10 May 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://isangfeng.github.io/tags/cca/index.xml" rel="self" type="application/rss+xml"/><item><title>典型相关分析CCA</title><link>https://isangfeng.github.io/posts/2023/05/practiceaboutcca/</link><pubDate>Wed, 10 May 2023 00:00:00 +0000</pubDate><guid>https://isangfeng.github.io/posts/2023/05/practiceaboutcca/</guid><description>&lt;p&gt;典型相关分析（Canonical Correlation Analysis, CCA）可以计算两组变量（每组变量包含多个变量）之间的相关。参考Pearson相关，它只能计算两个变量之间的相关。本文主要介绍笔者在使用CCA的过程中的理解，可能存在不准确的地方。详细的原理，请参考：&lt;a href="https://www.cnblogs.com/pinard/p/6288716.html" target="_blank" rel="noreferrer"&gt;https://www.cnblogs.com/pinard/p/6288716.html&lt;/a&gt;。&lt;/p&gt;</description></item></channel></rss>