Principal Components in TypeScript (Part 4)
This article is the fourth part of a series on Principal Components Analysis (PCA) in TypeScript. It explores the application of PCA for deriving named insights from data rather than just dimensionality reduction. The author discusses the process of using Singular Value Decomposition (SVD) to achieve interpretable dimensions from complex datasets.
- ▪The article is part four of a series focused on Principal Components Analysis in TypeScript.
- ▪It emphasizes using PCA to attribute causation to data through factor analysis.
- ▪The author explains how to compute correlations between original variables and factor scores to derive meaningful insights.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3890087) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } bitanath Posted on May 25 Principal Components in TypeScript (Part 4) #typescript #datascience #analytics Principal Components Analysis in Typescript (4 Part Series) 1 Principal Components in TypeScript (Part 1) 2 Principal Components in TypeScript (Part 2) 3 Principal Components in TypeScript (Part 3) 4 Principal Components in TypeScript (Part 4) This is part four of a series Principal Components in TypeScript and focuses on the application of PCA to actually derive named insights…
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