A tutorial explaining Principal Component Analysis (PCA), a dimensionality reduction technique used in machine learning and data analysis.
A guide to performing nonlinear dimensionality reduction using RBF Kernel PCA, including theory, implementation, and examples.
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.
