https://111qqz.com/en/tags/pca/Recent content in PCA on 111qqz's blogHugo -- gohugo.ioen© 2015-2026 111qqzSun, 26 Nov 2017 11:05:50 +0000https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/2017-11-26-pca-kmeans/Sun, 26 Nov 2017 11:05:50 +0000https://111qqz.com/en/post/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/2017-11-26-pca-kmeans/<h3 class="relative group">先记录一下PCA实战需要用到的安装包(arch下,python2环境) <div id="先记录一下pca实战需要用到的安装包arch下python2环境" class="anchor"></div> <span class="absolute top-0 w-6 transition-opacity opacity-0 -start-6 not-prose group-hover:opacity-100 select-none"> <a class="text-primary-300 dark:text-neutral-700 !no-underline" href="#%e5%85%88%e8%ae%b0%e5%bd%95%e4%b8%80%e4%b8%8bpca%e5%ae%9e%e6%88%98%e9%9c%80%e8%a6%81%e7%94%a8%e5%88%b0%e7%9a%84%e5%ae%89%e8%a3%85%e5%8c%85arch%e4%b8%8bpython2%e7%8e%af%e5%a2%83" aria-label="Anchor">#</a> </span> </h3> <h3 class="relative group"><a href="https://www.archlinux.org/packages/community/x86_64/python-scikit-learn/" target="_blank" rel="noreferrer">python2-scikit-learn</a> <div id="python2-scikit-learn" class="anchor"></div> <span class="absolute top-0 w-6 transition-opacity opacity-0 -start-6 not-prose group-hover:opacity-100 select-none"> <a class="text-primary-300 dark:text-neutral-700 !no-underline" href="#python2-scikit-learn" aria-label="Anchor">#</a> </span> </h3> <p>python2-numpy</p>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/Thu, 06 Jul 2017 08:35:51 +0000https://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/<p>说下我自己的理解</p> <p>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。</p>