#pca

2 articles

01 ·Linear Algebra·FREE·PAPER·11 min read PCA from Scratch — Eigenvectors as the Axes of Your Data A cloud of correlated data has a grain — a direction along which it varies most. Principal component analysis is the machinery that finds that grain. This article builds it from one picture: center the data, ask which direction carries the most variance, and discover that the answer is an eigenvector of the covariance matrix. Eigenvalues turn out to be the variance along each axis, the top few axes give you dimensionality reduction, and the explained-variance ratio tells you what you threw away. 02 ·Machine Learning Basics·★ MEMBER·PAPER·12 min read Unsupervised Learning from Scratch — Clustering and Dimensionality Reduction How to pull structure out of data that carries no labels at all, assuming nothing. What k-means, hierarchical clustering and DBSCAN are each good for, what PCA is actually maximizing, and the three ways people misread a t-SNE or UMAP picture — metaphor, then formula, then a figure you can move, then code.