An animated exploration of UMAP, a state-of-the-art dimensionality reduction algorithm, applied to the classic MNIST dataset of handwritten digits.
A tutorial explaining Principal Component Analysis (PCA), a dimensionality reduction technique used in machine learning and data analysis.
A tutorial explaining the internals of Principal Component Analysis (PCA) for dimensionality reduction in machine learning and data analysis.
A guide to performing nonlinear dimensionality reduction using RBF Kernel PCA, including theory, implementation, and examples.
Explains how to use the RBF kernel trick to perform nonlinear dimensionality reduction via Kernel PCA for complex datasets.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, with comparisons to PCA.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
A technical guide to implementing Principal Component Analysis (PCA) for dimensionality reduction, comparing it with MDA and providing code examples.
An author critiques the overuse of PCA in data science, arguing it's not a universal solution for classification problems.
