<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Principal Component Analysis - Tag - Zhiverse</title><link>https://yao-chih.netlify.app/en/tags/principal-component-analysis/</link><description>Principal Component Analysis - Tag - Zhiverse</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>yaochihh@gmail.com (Josh)</managingEditor><webMaster>yaochihh@gmail.com (Josh)</webMaster><copyright>Zhiverse</copyright><atom:link href="https://yao-chih.netlify.app/en/tags/principal-component-analysis/" rel="self" type="application/rss+xml"/><item><title>The Analysis of Characters in Dream of the Red Chamber</title><link>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</link><pubDate>Tue, 23 Dec 2025 10:28:34 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/the-analysis-of-characters-in-dream-of-the-red-chamber/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/The%20Analysis%20of%20Characters%20in%20Dream%20of%20the%20Red%20Chamber/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Classical Chinese novels are an indispensable and important part of Chinese literature, with many masterpieces throughout history worthy of in-depth exploration. Among them, Dream of the Red Chamber is regarded as one of the representative works of classical Chinese fiction and possesses a high degree of scholarly interest. The fates of the characters and the numerous events in the novel are intricately and closely interconnected, requiring thorough study to fully understand their true significance.</description></item><item><title>Principal Component Analysis (PCA)</title><link>https://yao-chih.netlify.app/en/principal-component-analysis/</link><pubDate>Tue, 25 Mar 2025 15:09:49 +0800</pubDate><author>Author</author><guid>https://yao-chih.netlify.app/en/principal-component-analysis/</guid><description>&lt;div class="featured-image">
                &lt;img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Principal%20Component%20Analysis/cover%20image.webp" referrerpolicy="no-referrer">
            &lt;/div>Principal Component Analysis (PCA) is a commonly used dimension reduction method. It transforms an originally linearly dependent dataset into a set of linearly independent new variables through an orthogonal transformation. This process minimizes the loss of information when projecting the dataset onto these new variables. These new variables are ranked based on their variance, with the one having the highest variance called the first principal component, the second highest called the second principal component, and so on. PCA is widely used to map high-dimensional data into lower-dimensional space while preserving as much of the original data’s key features and information as possible.</description></item></channel></rss>