Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
Explains how to use the RBF kernel trick to perform nonlinear dimensionality reduction via Kernel PCA for complex datasets.
Explains how to use the RBF kernel trick to perform nonlinear dimensionality reduction via Kernel PCA for complex datasets.
Explores using Principal Component Analysis on t-shirt images to build a gender classification model, visualizing data as 'eigenshirts'.
Explains the difference between ICA and PCA using scikit-learn code, advocating for runnable examples over static visuals in scientific materials.