Subject
6 entries
Dimensionality Reduction
Bookmarks
Models and Algorithms for Unlabelled Data
Vaibhav Verdhan's Manning book on unsupervised learning algorithms, covering clustering, dimensionality reduction, and anomaly detection with Python implementations on real-world datasets. Practical in orientation — stronger on applied case studies than mathematical rigor.
How UMAP Works
The official UMAP documentation explaining the mathematical intuition behind the algorithm — Riemannian geometry and fuzzy simplicial sets as the conceptual foundation. More rigorous than most t-SNE/UMAP comparisons and explains why UMAP preserves global structure better.
How to Use t-SNE Effectively
Martin Wattenberg, Fernanda Viégas, and Ian Johnson's interactive Distill article on how t-SNE maps actually work — and how to avoid misreading them. Essential reading before trusting any t-SNE plot.
Visualizing High-Dimensional Data in the Browser with SVD, t-SNE and Three.js
Datacratic's exploration of rendering high-dimensional data in the browser using SVD and t-SNE for dimensionality reduction and Three.js for 3D visualization. An early example of interactive ML visualization before dedicated tools like TensorBoard existed.
Introduction to Principal Component Analysis (PCA)
Laura Diane Hamilton's accessible introduction to Principal Component Analysis — covering the geometric and algebraic intuition without requiring a linear algebra background. One of the cleaner beginner explanations of a technique that's notoriously hard to explain well.
Kernel PCA
Sebastian Raschka's tutorial on Kernel PCA — extending standard PCA to capture non-linear structure using the kernel trick with RBF kernels. Includes Python implementation, making it one of the clearest practical explanations of the technique available in 2014.