Unsupervised and Supervised Principal Component Analysis: Tutorial
This is an incremental tutorial paper for researchers and practitioners learning about PCA and its extensions.
This tutorial paper explains Principal Component Analysis (PCA) and its supervised variants, covering theoretical foundations and practical implementations with simulations on Frey and AT&T face datasets to verify the methods.
This is a detailed tutorial paper which explains the Principal Component Analysis (PCA), Supervised PCA (SPCA), kernel PCA, and kernel SPCA. We start with projection, PCA with eigen-decomposition, PCA with one and multiple projection directions, properties of the projection matrix, reconstruction error minimization, and we connect to autoencoder. Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered. SPCA using both scoring and Hilbert-Schmidt independence criterion are explained. Kernel SPCA using both direct and dual approaches are then introduced. We cover all cases of projection and reconstruction of training and out-of-sample data. Finally, some simulations are provided on Frey and AT&T face datasets for verifying the theory in practice.