Dimensionality Reduction: PCA and Kernel PCA

This lecture covers the concepts of Principal Component Analysis (PCA) and Kernel PCA for dimensionality reduction, using examples from the Cancer Genome Atlas breast cancer RNA-Seq dataset. It explains how PCA reduces data dimensionality and provides insights into the influence of attributes on components. Additionally, it introduces Kernel PCA as a method to handle nonlinear data and discusses its application in feature space. The lecture also explores autoencoders as a nonlinear alternative to PCA, including deep autoencoders, denoising autoencoders, and sparse autoencoders. It concludes with a demonstration of autoencoder applications for image retrieval and data generation.